mayank31398/Papers-books-and-blogs

This repository contains the research papers, white papers, thesis etc that I love.

This repository contains a list of the books, blogs, research papers and white papers that I have read and found interesting.

Table of contents

• AI, DL, NLP and RL
• Calculus
• Computer Architecture
• Computer Graphics
• Data Structures and Algorithms
• Digital Electronics
• Graph Theory
• Information Theory
• Linear Algebra
• Measure Theory
• Optimization Theory
• Probability and Stochastic Processes
• Quantum Computing
• Signal Processing

AI, DL, NLP and RL

1. 1-bit Adam: communication efficient large-scale training with Adam’s convergence speed
   Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He
   
2. 5 best practices for efficient model training
   Matthew Leavitt, Abhinav Venigalla
   
3. 8-bit approximations for parallelism in deep learning
   Tim Dettmers
   
4. 8-bit optimizers via block-wise quantization
   Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer
   
5. A 'neural' network that learns to play Backgammon
   Gerald Tesauro, Terrence J. Sejnowski
   
6. A BetterTransformer for fast transformer inference
   Michael Gschwind, Eric Han, Scott Wolchok, Rui Zhu, Christian Puhrsch
   
7. A deep reinforced model for abstractive summarization
   Romain Paulus, Caiming Xiong, Richard Socher
   
8. A dynamical approach to temporal pattern processing
   W. Scott Stornetta, Tad Hogg, Bernardo A. Huberman
   
9. A few more examples may be worth billions of parameters
   Yuval Kirstain, Patrick Lewis, Sebastian Riedel, Omer Levy
   
10. A general and adaptive robust loss function
    Jonathan T. Barron
    
11. A gentle introduction to 8-bit matrix multiplication for transformers at scale using Hugging Face transformers, accelerate and bitsandbytes
    Younes Belkada, Tim Dettmers
    
12. A note on the evaluation of generative models
    Lucas Theis, Aäron van den Oord, Matthias Bethge
    
13. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
    Mikel Artetxe, Gorka Labaka, Eneko Agirre
    
14. A simple but tough-to-beat baseline for sentence embeddings
    Sanjeev Arora, Yingyu Liang, Tengyu Ma
    
15. A simple language model for task-oriented dialogue
    Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher
    
16. A simple neural attentive meta-learner
    Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel
    
17. A simple neural network module for relational reasoning
    Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap
    
18. A study of BFLOAT16 for deep learning training
    Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey
    
19. A style-based generator architecture for generative adversarial networks
    Tero Karras, Samuli Laine, Timo Aila
    
20. A stylometric inquiry into hyperpartisan and fake news
    Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, Benno Stein
    
21. A3T: adversarially augmented adversarial training
    Akram Erraqabi, Aristide Baratin, Yoshua Bengio, Simon Lacoste-Julien
    
22. Accelerated PyTorch 2 transformers
    Michael Gschwind, Driss Guessous, Christian Puhrsch
    
23. Accelerating large language model training with variable sparse pre-training and dense fine-tuning
    Abhay Gupta, Mahmoud Salem, Vithursan Thangarasa, Kevin Leong, Sean Lie, Shreyas Saxena
    
24. Accelerating PyTorch with CUDA graphs
    Vinh Nguyen, Michael Carilli, Sukru Burc Eryilmaz, Vartika Singh, Michelle Lin, Natalia Gimelshein, Alban Desmaison, Edward Yang
    
25. AdapterHub: a framework for adapting transformers
    Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych
    
26. Adversarial approximate inference for speech to electroglottograph conversion
    Prathosh A. P., Varun Srivastava, Mayank Mishra
    
27. Adversarial autoencoders
    Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey
    
28. Adversarial examples that fool both computer vision and time-limited humans
    Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein
    
29. Adversarial feature learning
    Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
    
30. Adversarial generation of natural language
    Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville
    
31. Adversarial information factorization
    Antonia Creswell, Yumnah Mohamied, Biswa Sengupta, Anil A Bharath
    
32. Adversarially learned inference
    Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville
    
33. AlexaTM 20B: few-shot learning using a large-scale multilingual seq2seq model
    Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan
    
34. Amazon SageMaker model parallelism: a general and flexible framework for large model training
    Can Karakus, Rahul Huilgol, Fei Wu, Anirudh Subramanian, Cade Daniel, Derya Cavdar, Teng Xu, Haohan Chen, Arash Rahnama, Luis Quintela
    
35. An image is worth 16x16 words: transformers for image recognition at scale
    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
    
36. An overview of gradient descent optimization algorithms
    Sebastian Ruder
    
37. Analysing mathematical reasoning abilities of neural models
    David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
    
38. Approximation by superpositions of sigmoidal function
    George Cybenko
    
39. Artificial Intelligence: a modern approach
    Stuart Russell, Peter Norvig
    
40. Aspect based sentiment analysis with gated convolutional networks
    Wei Xue, Tao Li
    
41. Attention is all you need
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
    
42. Attention is off by one
    Evan Miller
    
43. Auto-encoding variational Bayes
    Diederik P. Kingma, Max Welling
    
44. Backpropagation through the void: optimizing control variates for black-box gradient estimation
    Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
    
45. BART: denoising sequence-to-sequence pre-training for natural language generation, translation and comprehension
    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer
    
46. Batch normalization: accelerating deep network training by reducing internal covariate shift
    Sergey Ioffe, Christian Szegedy
    
47. Behavioral cloning from observation
    Faraz Torabi, Garrett Warnell, Peter Stone
    
48. BERT: pre-training of deep bidirectional transformers for language understanding
    Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
    
49. Beyond domain APIs: Task-oriented conversational modeling with unstructured knowledge access
    Seokhwan Kim, Mihail Eric, Karthik Gopalakrishnan, Behnam Hedayatnia, Yang Liu, Dilek Hakkani-Tur
    
50. Blockwise parallel transformer for large context models
    Hao Liu, Pieter Abbeel
    
51. BLOOM: A 176B-parameter open-access multilingual language model
    Aaron Gokaslan, Abheesht Sharma, Abhinav Ramesh Kashyap, Adam Roberts, Adi Simhi, Ahmed Baruwa, Aitor Soroa, Albert Villanova del Moral, Albert Webson, Alexander M. Rush, Alexandra Sasha Luccioni, Alfredo Palasciano, Alham Fikri Aji, Alice Rueda, Alison Callahan, Amanda Pestana, Amanpreet Singh, Amir Feizpour, Amit Alfassy, Ammar Khan, Amy Faranak, Ana Santos, Anastasia Cheveleva, Andrea Santilli, Angela Fan, Angelina McMillan-Major, Anima Shukla, Anna Rogers, Anne-Laure Ligozat, Anthony Hevia, Antigona Unldreaj, Antoine Chaffin, Antonio Miranda-Escalada, Arash Aghagol, Arezoo Abdollahi, Ariel Kreisberg Nitzav, Arjun Subramonian, Arnaud Stiegler, Arun Raja, Aurélie Névéol, Aycha Tammour, Ayush Singh, Azadeh HajiHosseini, Bahareh Behroozi, Benjamin Ajibade, Benjamin Beilharz, Benjamin Heinzerling, Benoît Sagot, Bharat Saxena, Bo Wang, Caio Brito, Canwen Xu, Carlos Muñoz Ferrandis, Charles Lovering, Chenghao Mou, Chenglei Si, Chenxi Zhou, Chirag Jain, Chris Emezue, Christopher Akiki, Christopher Klamm, Chuxin Xu, Clémentine Fourrier, Colin Leong, Colin Raffel, Conglong Li, Dan Garrette, Daniel Hesslow, Daniel León Periñán, Daniel Molano, Daniel van Strien, Danish Contractor, David Ifeoluwa Adelani, David Lansky, Davis David, Davut Emre Taşar, Debajyoti Datta, Deepak Narayanan, Deepak Tunuguntla, Dian Yu, Douwe Kiela, Dragomir Radev, Duong A. Nguyen, Eduardo González Ponferrada, Edward Tan, Efrat Levkovizh, Ehud Reiter, Ekaterina Taktasheva, Ekaterina Voloshina, Eli Bogdanov, Eliza Szczechla, Elizabeth Salesky, Ellie Pavlick, Emi Baylor, Enrique Manjavacas, Ethan Kim, Eyal Bar Natan, Ezinwanne Ozoani, Fabio Barth, Fatima Mirza, Florian Fuhrimann, Francesco De Toni, Frankline Ononiwu, François Yvon, Gabriel Altay, Genta Indra Winata, Germán Kruszewski, Giada Pistilli, Giyaseddin Bayrak, Gully Burns, Gunjan Chhablani, Gérard Dupont, Habib Rezanejad, Hadar Tojarieh, Hady Elsahar, Hailey Schoelkopf, Hamza Benyamina, Han Wang, Harshit Pandey, Hatim Bourfoune, Helena U. Vrabec, Hendrik Strobelt, Hessie Jones, Hieu Tran, Hugo Laurençon, Huu Nguyen, Hyung Won Chung, Ian Yu, Idris Abdulmumin, Imane Bello, Indrani Bhattacharya, Irene Solaiman, Irina Sedenko, Isaac Johnson, Isar Nejadgholi, Ishani Dash, Itziar Gonzalez-Dios, Iz Beltagy, Jaesung Tae, Jan-Christoph Kalo, Jared Casper, Jason Alan Fries, Jason Phang, Javier de la Rosa, Jeff Rasley, Jekaterina Novikova, Jenny Chim, Jesse Dodge, Jesse Passmore, Jessica Zosa Forde, Jian Zhu, Jihyun Kang, John Giorgi, Jonas Golde, Jonathan Chang, Jonathan Tow, Jordan Clive, Jos Rozen, Jose David Posada, Joseph Tobing, Josh Seltzer, Joydeep Bhattacharjee, Julien Launay, Julio Bonis Sanz, Jungo Kasai, Jörg Frohberg, Karthik Rangasai Sivaraman, Ken Kawamura, Khalid Almubarak, Kimbo Chen, Kyle Lo, Leandro Von Werra, Leo Gao, Leon Weber, Liam Hazan, Lintang Sutawika, Livia Dutra, Lokesh Bulchandani, Long Phan, Loubna Ben allal, Lu Liu, Lucile Saulnier, Ludovic Tanguy, Luisa Shinzato, M Saiful Bari, Madeleine Hahn de Bykhovetz, Maged S. Al-shaibani, Maiko Takeuchi, Mairon Samagaio, Manan Dey, Manuel Romero Muñoz, Maraim Elbadri, Maraim Masoud, Marc Pàmies, Margaret Mitchell, Margot Mieskes, Maria A Castillo, Marianna Nezhurina, Marine Carpuat, Mario Sänger, Mario Šaško, Marissa Gerchick, Martha Akinlolu, María Grandury, Mathilde Bras, Matteo Manica, Matthias Gallé, Matthias Samwald, Max Huang, Max Ryabinin, Maximin Coavoux, Mayank Mishra, Mayank Singh, Michael Cullan, Michael McKenna, Michael Weinberg, Michiel De Wolf, Mike Qiu, Mike Tian-Jian Jiang, Mina Mihaljcic, Minh Chien Vu, Minjia Zhang, Minna Liu, Miruna Clinciu, Mohammad A. Jauhar, Mohammad Shoeybi, Moritz Freidank, Muhammed Ghauri, Mustafa Ghaleb, Mykola Burynok, Myriam Peyrounette, Myungsun Kang, Nafis Abrar, Najoung Kim, Natasha Seelam, Nathan Dahlberg, Nazneen Rajani, Newton Cheng, Nicholas Michio Broad, Nicolas Patry, Nihal Nayak, Niklas Muennighoff, Nikolaus Muellner, Nishant Subramani, Nora Kassner, Nouamane Tazi, Nour Elkott, Nour Fahmy, Nurulaqilla Khamis, Ofir Press, Olanrewaju Samuel, Olatunji Ruwase, Oleg Serikov, Olivier Nguyen, Omar Espejel, Omar Sanseviero, Omer Antverg, Ona de Gibert, Oskar van der Wal, Pascale Fung, Patrick Haller, Patrick von Platen, Paulo Villegas, Pawan Sasanka Ammanamanchi, Pedro Ortiz Suarez, Peter Henderson, Pierre Colombo, Pierre Cornette, Pierre François Lavallée, Priscilla Amuok, Quentin Lhoest, Rachel Bawden, Ramya Chandrasekhar, Ran An, Rasmus Kromann, Renata Eisenberg, Rheza Harliman, Rishi Bommasani, Robert Martin, Roberto Luis López, Rodrigo Canalli, Roman Castagné, Rosaline Su, Rui Ribeiro, Rui Zhang, Ruisi Su, Ruochen Zhang, Ryan Hao, Ryan Teehan, Rémi Lacroix, Sabrina J. Mielke, Salomey Osei, Samira Alizadeh, Sampo Pyysalo, Samson Tan, Samuel Albanie, Samuel Cahyawijaya, Samuele Garda, Samyam Rajbhandari, Sanchit Gandhi, Sarmad Shubber, Sebastian Gehrmann, Sebastian Nagel, Shachar Mirkin, Shaden Smith, Shaked Brody, Shamik Bose, Shamsuddeen Hassan Muhammad, Shani Pais, Shanya Sharma, Shayne Longpre, Sheng Shen, Shlok S Deshmukh, Shubhanshu Mishra, Sid Kiblawi, Silas Wang, Simon Ott, Sinee Sang-aroonsiri, Somaieh Nikpoor, Sourav Roy, Srishti Kumar, Srulik Ben-David, Stanislav Silberberg, Stas Bekman, Stefan Schweter, Stella Biderman, Stephen H. Bach, Stéphane Requena, Suhas Pai, Suraj Patil, Sushil Bharati, Suzana Ilić, Sydney Zink, Sylvain Viguier, Taewoon Kim, Tali Bers, Tanmay Laud, Tatiana Shavrina, Teven Le Scao, Thanh Le, Thibault Fevry, Thomas Scialom, Thomas Wang, Thomas Wolf, Théo Gigant, Tiago Timponi Torrent, Tian Yun, Tim Dettmers, Timo Schick, Tobi Oyebade, Tomasz Limisiewicz, Tomoya Kainuma, Trieu Le, Trishala Neeraj, Tristan Thrush, Urmish Thakker, Valentin Danchev, Vassilina Nikoulina, Verena Rieser, Veronika Laippala, Victor Sanh, Vikas Raunak, Violette Lepercq, Vitaly Protasov, Vladislav Mikhailov, Vrinda Prabhu, Wilson Y. Lee, Wojciech Kusa, Xiangru Tang, Yacine Jernite, Yada Pruksachatkun, Yallow Uri, Yanis Labrak, Yash Shailesh Bajaj, Yash Venkatraman, Yifan Xu, Yingxin Xu, Yonatan Belinkov, Younes Belkada, Yoyo Yang, Yu Xu, Zach Nguyen, Zachary Bamberger, Zaid Alyafeai, Zdeněk Kasner, Zeerak Talat, Zhe Tan, Zheng-Xin Yong, Zhiqing Sun, Zhongli Xie, Zifan Ye
    
52. Bootstrapping entity alignment with knowledge graph embedding
    Zequn Sun, Wei Hu, Qingheng Zhang, Yuzhong Qu
    
53. Bridging the gap between prior and posterior knowledge selection for knowledge-grounded dialogue generation
    Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie Zhou
    
54. Bringing open large language models to consumer devices
    MLC Community
    
55. BTLM-3B-8K: 7B performance in a 3 billion parameter model
    Nolan Dey, Daria Soboleva, Faisal Al-Khateeb, Ribhu Pathria, Hemant Khachane, Shaheer Muhammad, Zhiming (Charles) Chen, Bowen Yang, Siyun Li, Abhay Gupta, Shreyas Saxena, Robert Myers, Jacob Robert Steeves, Marvin Tom, Joel Hestness
    
56. Building blocks for a complex-valued transformer architecture
    Florian Eilers, Xiaoyi Jiang
    
57. ChatGPT: optimizing language models for dialogue
    
58. ColBERT: efficient and effective passage search via contextualized late interaction over BERT
    Omar Khattab, Matei Zaharia
    
59. Colossal-AI: a unified deep learning system for large-scale parallel training
    Zhengda Bian, Hongxin Liu, Boxiang Wang, Haichen Huang, Yongbin Li, Chuanrui Wang, Fan Cui, Yang You
    
60. Compiling machine learning programs via high-level tracing
    Roy Frostig, Matthew Johnson, Chris Leary
    
61. Complex transformer: a framework for modeling complex-valued sequence
    Muqiao Yang, Martin Q. Ma, Dongyu Li, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov
    
62. Conceptual captions: a cleaned, hypernymed, image alt-text dataset for automatic image captioning
    Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut
    
63. Conditional image synthesis with auxilliary classifier GANs
    Augustus Odena, Christopher Olah, Jonathon Shlens
    
64. Conformal nucleus sampling
    Shauli Ravfogel, Yoav Goldberg, Jacob Goldberger
    
65. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
    Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang
    
66. Connectivity versus entropy
    Yaser S. Abu-Mostafa
    
67. Constituency parsing with a self-attentive encoder
    Nikita Kitaev, Dan Klein
    
68. Constraint based knowledge base distillation in end-to-end task oriented dialogs
    Dinesh Raghu, Atishya Jain, Mausam, Sachindra Joshi
    
69. Context generation improves open domain question answering
    Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
    
70. Convert transformers to ONNX with hugging face optimum
    Philipp Schmid
    
71. Convolutional networks for graphs for learning molecular fingerprints
    David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams
    
72. Convolutional neural network language models
    Ngoc-Quan Pham, Germán Kruszewski, Gemma Boleda
    
73. Countering adversarial images using input transformations
    Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten
    
74. Cramming: training a language model on a single GPU in one day
    Jonas Geiping, Tom Goldstein
    
75. Crosslingual generalization through multitask finetuning
    Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
    
76. Curriculum learning
    Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston
    
77. Cutting down on prompts and parameters: simple few-shot learning with language models
    Robert L. Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel
    
78. Deep Boltzmann machines
    Ruslan Salakhutdinov, Geoffrey Hinton
    
79. Deep complex networks
    Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J Pal
    
80. Deep learning
    Ian Goodfellow, Yoshua Bengio, Aaron Courville
    
81. Deep learning and the information bottleneck principle
    Naftali Tishby, Noga Zaslavsky
    
82. Deep learning techniques for super-resolution in video games
    Alexander Watson
    
83. Deep residual learning for image recognition
    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    
84. Deep text classification can be fooled
    Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi
    
85. DeepSpeed compression: a composable library for extreme compression and zero-cost quantization
    DeepSpeed Team, Andrey Proskurin
    
86. DeepSpeed Inference: enabling efficient inference of transformer models at unprecedented scale
    Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He
    
87. DeepSpeed powers 8x larger MoE model training with high performance
    DeepSpeed Team, Z-code Team
    
88. DeepSpeed: accelerating large-scale model inference and training via system optimizations and compression
    DeepSpeed Team, Rangan Majumder, Andrey Proskurin
    
89. DeepSpeed: advancing MoE inference and training to power next-generation AI scale
    DeepSpeed Team, Andrey Proskurin
    
90. Denoising distantly supervised open-domain question answering
    Yankai Lin, Haozhe Ji, Zhiyuan Liu, Maosong Sun
    
91. Diffusion convolutional recurrent neural network: data-driven traffic forecasting
    Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu
    
92. Discrete variational autoencoders
    Jason Tyler Rolfe
    
93. Disentangling by factorising
    Hyunjik Kim, Andriy Mnih
    
94. Disentangling language and knowledge in task-oriented dialogs
    Dinesh Raghu, Nikhil Gupta, Mausam
    
95. Distributionally robust language modeling
    Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto, Percy Liang
    
96. Editing models with task arithmetic
    Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi
    
97. Efficient estimation of word representations in vector space
    Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
    
98. Efficient large scale language modeling with mixtures of experts
    Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov
    
99. Efficient large-scale language model training on GPU clusters using Megatron-LM
    Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
    
100. Enchancing the reliability of out-of-distribution image detection in neural networks
     Shiyu Liang, Yixuan Li, R. Srikant
     
101. End-to-end task-oriented dialog modeling with semi-structured knowledge management
     Silin Gao, Ryuichi Takanobu, Antoine Bosselut, Minlie Huang
     
102. Ensemble adversarial training: attacks and defenses
     Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel
     
103. Equilibrium propagation: bridging the gap between energy-based models and backpropagation
     Benjamin Scellier, Yoshua Bengio
     
104. Estimating or propagating gradients through stochastic neurons for conditional computation
     Yoshua Bengio, Nicholas Léonard, Aaron Courville
     
105. Exemplar encoder-decoder for neural conversation generation
     Gaurav Pandey, Danish Contractor, Vineet Kumar, Sachindra Joshi
     
106. Expert human-level driving in gran turismo sport using deep reinforcement learning with image-based representation
     Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger
     
107. Exploring deep recurrent models with reinforcement learning for molecule design
     Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown
     
108. Exploring the limits of transfer learning with a unified text-to-text transformer
     Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu
     
109. Extreme compression for pre-trained transformers made simple and efficient
     Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He
     
110. Fast abstractive summarization with reinforce-selected sentence rewriting
     Yen-Chun Chen, Mohit Bansal
     
111. Fast transformer decoding: one write-head is all you need
     Noam Shazeer
     
112. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
     Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel
     
113. FFJORD: Free-form continuous dynamics for scalable reversible generative models
     Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
     
114. Finetuned language models are zero-shot learners
     Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le
     
115. Flash-decoding for long-context inference
     Tri Dao, Daniel Haziza, Francisco Massa, Grigory Sizov
     
116. FlashAttention: fast and memory-efficient exact attention with IO-awareness
     Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré
     
117. FlashAttention: fast transformer training with long sequences
     Tri Dao
     
118. Foundations of NLP explained visually: beam search, how it works
     Ketan Doshi
     
119. Generating adversarial examples with adversarial networks
     Chaowei Xiao, Bo Li, Jun-yan Zhu, Warren He, Mingyan Liu, Dawn Song
     
120. Generating sentences from a continuous space
     Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio
     
121. Generation-augmented retrieval for open-domain question answering
     Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen
     
122. Generative adversarial nets
     Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
     
123. Genetic algorithms in search, optimization and machine learning
     David E. Goldberg
     
124. GeoMAN: multi-level attention networks for geo-sensory time series prediction
     Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng
     
125. Getting the most out of the NVIDIA A100 GPU with Multi-Instance GPU
     Maggie Zhang, James Sohn, Chetan Tekur
     
126. GLaM: efficient scaling of language models with mixture-of-experts
     Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui
     
127. GLM-130B: an open bilingual pre-trained model
     
128. GLU variants improve transformer
     Noam Shazeer
     
129. Going deeper with convolutions
     Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
     
130. GPT-4 architecture, infrastructure, training dataset, costs, vision, MoE
     Dylan Patel, Gerald Wong
     
131. GPT-NeoX-20B: an open-source autoregressive language model
     Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
     
132. GQA: training generalized multi-query transformer models from multi-head checkpoints
     Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai
     
133. Gradient-based hyperparameter optimization through reversible learning
     Dougal Maclaurin, David Duvenaud, Ryan P. Adams
     
134. Graph attention networks
     Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
     
135. Hierarchical neural story generation
     Angela Fan, Mike Lewis, Yann Dauphin
     
136. Hindsight: posterior-guided training of retrievers for improved open-ended generation
     Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning
     
137. HotFlip: white-box adversarial examples for text classification
     Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou
     
138. How big should my language model be?
     Teven Le Scao
     
139. How should AI systems behave, and who should decide?
     
140. How we sped up transformer inference 100x for 🤗 API customers
     
141. How 🤗 Accelerate runs very large models thanks to PyTorch
     Sylvain Gugger
     
142. HyKnow: end-to-end task-oriented dialog modeling with hybrid knowledge management
     Silin Gao, Ryuichi Takanobu, Wei Peng, Qun Liu, Minlie Huang
     
143. Hyperparameter search with Transformers and Ray Tune
     
144. Image-to-image translation with conditional generative adversarial networks
     Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
     
145. ImageNet classification using deep convolutional neural networks
     Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
     
146. Improving entity linking by modeling latent relations between mentions
     Phong Le, Ivan Titov
     
147. Improving language models by retrieving from trillions of tokens
     Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre
     
148. Improving language understanding by generative pre-training
     Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever
     
149. Improving reinforcement learning from human feedback with efficient reward model ensemble
     Shun Zhang, Zhenfang Chen, Sunli Chen, Yikang Shen, Zhiqing Sun, Chuang Gan
     
150. Incredibly fast BLOOM inference with DeepSpeed and Accelerate
     Stas Bekman, Sylvain Gugger
     
151. Inference suboptimality in variational autoencoders
     Chris Cremer, Xuechen Li, David Duvenaud
     
152. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets
     Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
     
153. Interpretable convolutional neural networks via feedforward design
     C.-C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, Yueru Chen
     
154. Introducing MPT-7B: a new standard for open-source, commercially usable LLMs
     The MosaicML NLP Team
     
155. Introducing nvFuser, a deep learning compiler for PyTorch
     Christian Sarofeen, Piotr Bialecki, Jie Jiang, Kevin Stephano, Masaki Kozuki, Neal Vaidya, Stas Bekman
     
156. Introducing Turing image super resolution: AI powered image enhancements for Microsoft Edge and Bing maps
     
157. Introducing 🤗 accelerate
     Sylvain Gugger
     
158. Is ChatGPT 175 billion parameters? Technical analysis
     Oren Leung
     
159. Is the future of neural networks Sparse? An introduction (1/N)
     François Lagunas
     
160. Joint reasoning on hybrid-knowledge sources for task-oriented dialog
     Mayank Mishra, Danish Contractor, Dinesh Raghu
     
161. Know what you don't know: unanswerable questions for SQuAD
     Pranav Rajpurkar, Robin Jia, Percy Liang
     
162. Knowledge-grounded dialogue generation with pre-trained language models
     Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan
     
163. Language is not all you need: aligning perception with language models
     Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, Furu Wei
     
164. Language modeling with gated convolutional networks
     Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier
     
165. Language modelling with pixels
     Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
     
166. Language models (mostly) know what they know
     Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, Jared Kaplan
     
167. Language models are unsupervised multitask learners
     Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
     
168. Large language models are not fair evaluators
     Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, Zhifang Sui
     
169. Layer normalization
     Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
     
170. Learning activation functions to improve deep neural networks
     Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi
     
171. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
     Tiancheng Zhao, Ran Zhao, Maxine Eskenazi
     
172. Learning on a general network
     Amir F. Atiya
     
173. Learning representations by back-propagating errors
     David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams
     
174. Learning transferable visual models from natural language supervision
     Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever
     
175. Learning word embeddings efficiently with noise-contrastive estimation
     Andriy Mnih, Koray Kavukcuoglu
     
176. Lessons learned on language model safety and misuse
     
177. Lifelong language pretraining with distribution-specialized experts
     Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, Claire Cu
     
178. Linear scaling made possible with weight streaming
     Andrew Feldman
     
179. Linformer: self-attention with linear complexity
     Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, Hao Ma
     
180. LLM in a flash: efficient large language model inference with limited memory
     Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko, Karen Khatamifard, Minsik Cho, Carlo C Del Mundo, Mohammad Rastegari, Mehrdad Farajtabar
     
181. LLM.int8(): 8-bit matrix multiplication for transformers at scale
     Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer
     
182. Long sequence modeling with XGen: a 7B LLM trained on 8K input sequence length
     Erik Nijkamp, Hiroaki Hayashi, Tian Xie, Congying Xia, Bo Pang, Rui Meng, Wojciech Kryscinski, Lifu Tu, Meghana Bhat, Semih Yavuz, Chen Xing, Jesse Vig, Lidiya Murakhovs'ka, Chien-Sheng Wu, Yingbo Zhou, Shafiq Rayhan Joty, Caiming Xiong, Silvio Savarese
     
183. LoRA: Low-Rank Adaptation of large language models
     Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen
     
184. Lost in the middle: how language models use long contexts
     Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang
     
185. M6-10T: a sharing-delinking paradigm for efficient multi-trillion parameter pretraining
     Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang
     
186. Machine learning
     Tom M. Mitchell
     
187. Machine learning: a probabilistic perspective
     Kevin P. Murphy
     
188. Making deep learning go brrrr from first principles
     Horace He
     
189. Making DeepSpeed ZeRO run efficiently on more-affordable hardware
     Justin Chiu, Shuai Zheng
     
190. Mask & focus: conversation modelling by learning concepts
     Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
     
191. Maximizing communication efficiency for large-scale training via 0/1 Adam
     Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He
     
192. MCR-DL: mix-and-match communication runtime for deep learning
     Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda
     
193. MegaBlocks: efficient sparse training with mixture-of-experts
     Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia
     
194. Megatron-LM: training multi-billion parameter language models using model parallelism
     Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro
     
195. Memory-efficient pipeline-parallel DNN training
     Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
     
196. MinTL: minimalist transfer learning for task-oriented dialogue systems
     Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung
     
197. Mix and match: learning-free controllable text generation using energy language models
     Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick
     
198. Mixed precision training
     Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu
     
199. Mixture of attention heads: selecting attention heads per token
     Xiaofeng Zhang, Yikang Shen, Zeyu Huang, Jie Zhou, Wenge Rong, Zhang Xiong
     
200. Mixture-of-Experts meets instruction tuning: a winning combination for large language models
     Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, Denny Zhou
     
201. mixup: beyond empirical risk minimization
     Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
     
202. MMCoQA: conversational question answering over text, tables and images
     Yongqi Li, Wenjie Li, Liqiang Nie
     
203. Mode matching in GANs through latent space learning and inversion
     Deepak Mishra, Prathosh A. P., Aravind Jayendran, Varun Srivastava, Santanu Chaudhury
     
204. Multi-level memory for task oriented dialogs
     Revanth Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi
     
205. Multitask prompt tuning enables parameter-efficient transfer learning
     Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Huan Sun, Yoon Kim
     
206. MultiWOZ - A large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling
     Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gašić
     
207. Mutual information neural estimation
     Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R Devon Hjelm
     
208. NeMo: a toolkit for building AI applications using neural modules
     Oleksii Kuchaiev, Jason Li, Huyen Nguyen, Oleksii Hrinchuk, Ryan Leary, Boris Ginsburg, Samuel Kriman, Stanislav Beliaev, Vitaly Lavrukhin, Jack Cook, Patrice Castonguay, Mariya Popova, Jocelyn Huang, Jonathan M. Cohen
     
209. Neural GPUs learn algorithms
     Łukasz Kaiser, Ilya Sutskever
     
210. Neural network methods for natural language processing
     Yaov Goldberg
     
211. Neural networks and physical systems with emergent collective computational abilities
     J. J. Hopfield
     
212. Neural networks for pattern recognition
     Christopher M. Bishop
     
213. Neural ordinary differential equations
     Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
     
214. No train no gain: revisiting efficient training algorithms for transformer-based language models
     Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner
     
215. Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples
     Anish Athalye, Nicholas Carlini, David Wagner
     
216. OctoPack: instruction tuning code large language models
     Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre
     
217. On the convergence of Adam and beyond
     Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
     
218. On the power of neural networks for solving hard problems
     Jehoshua Bruck, Joseph W. Goodman
     
219. One model to learn them all
     Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, Jakob Uszkoreit
     
220. Open domain question answering over tables via dense retrieval
     Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Eisenschlos
     
221. Open question answering over tables and text
     Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, William W. Cohen
     
222. OPT: open pre-trained transformer language models
     Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, Luke Zettlemoyer
     
223. Optimal brain compression: a framework for accurate post-training quantization and pruning
     Elias Frantar, Sidak Pal Singh, Dan Alistarh
     
224. Optimal perceptual inference
     Geoffrey E. Hinton, Terrence J. Sejnowski
     
225. Optimization story: Bloom inference
     Nicolas Patry
     
226. Orca 2: teaching small language models how to reason
     Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas, Clarisse Simoes, Sahaj Agarwal, Xuxi Chen, Anastasia Razdaibiedina, Erik Jones, Kriti Aggarwal, Hamid Palangi, Guoqing Zheng, Corby Rosset, Hamed Khanpour, Ahmed Awadallah
     
227. Orca: progressive learning from complex explanation traces of GPT-4
     Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah
     
228. Outer product-based neural collaborative filtering
     Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua
     
229. Outrageously large neural networks: the sparsely-gated mixture-of-experts layer
     Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean
     
230. Overcoming oscillations in quantization-aware training
     Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort
     
231. PAL: Program-aided language models
     Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig
     
232. PaLM: scaling language modeling with pathways
     Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel
     
233. Parallel context windows improve in-context learning of large language models
     Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
     
234. Pattern classification
     Richard O. Duda, Peter E. Hart, David G. Stork
     
235. Pattern recognition and machine learning
     Christopher M. Bishop
     
236. Perceptual losses for real-time style transfer and super-resolution
     Justin Johnson, Alexandre Alahi, Li Fei-Fei
     
237. Personalizing dialogue agents: I have a dog, do you have pets too?
     Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, Jason Weston
     
238. Phase-functioned neural networks for character control
     Daniel Holden, Taku Komura, Jun Saito
     
239. Playing Atari with deep reinforcement learning
     Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
     
240. Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing
     Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig
     
241. Prefix-tuning: optimizing continuous prompts for generation
     Xiang Lisa Li, Percy Liang
     
242. Probabilistic latent semantic analysis
     Thomas Hofmann
     
243. Progressive growing of GANs from improved quality, stability and variation
     Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen
     
244. Prompting with pseudo-code instructions
     Mayank Mishra, Prince Kumar, Riyaz Bhat, Rudra Murthy V, Danish Contractor, Srikanth Tamilselvam
     
245. PullNet: open domain question answering with iterative retrieval on knowledge bases and text
     Haitian Sun, Tania Bedrax-Weiss, William Cohen
     
246. PyTorch trace analysis for the masses
     Anupam Bhatnagar, Xizhou Feng, Brian Coutinho, Yifan Liu, Sung-Han Lin, Louis Feng, and Yuzhen Huang
     
247. Q-BERT: Hessian based ultra low precision quantization of BERT
     Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer
     
248. R³Net: recurrent residual refinement network for saliency detection
     Zijun Deng, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Jing Qin, Guoqiang Han, Pheng-Ann Heng
     
249. Reading Wikipedia to answer open-domain questions
     Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
     
250. REALM: Retrieval-augmented language model pretraining
     Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang
     
251. Recurrent models of visual attention
     Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
     
252. Reducing activation recomputation in large transformer models
     Vijay Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, Bryan Catanzaro
     
253. Regularizing and optimizing LSTM language models
     Stephen Merity, Nitish Shirish Keskar, Richard Socher
     
254. Reinforcement Learning: An Introduction
     Richard S. Sutton, Andrew G. Barto
     
255. ReLoRA: high-rank training through low-rank updates
     Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira, Anna Rumshisky
     
256. Restricted Boltzmann machines for collaborative filtering
     Ruslan Salakhutdinov, Andriy Mnih, Geoffrey Hinton
     
257. Retrieval augmentation reduces hallucination in conversation
     Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, Jason Weston
     
258. Retrieval-augmented generation for knowledge-intensive NLP tasks
     Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela
     
259. Revisiting classifier two-sample tests
     David Lopez-Paz, Maxime Oquab
     
260. RoBERTa: a robustly optimized BERT pretraining approach
     Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov
     
261. RoFormer: enhanced transformer with rotary position embedding
     Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, Yunfeng Liu
     
262. SantaCoder: don't reach for the stars!
     Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra
     
263. Scaling instruction-finetuned language models
     Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei
     
264. Scaling PyTorch FSDP for training foundation Models on IBM cloud
     Linsong Chu, Less Wright, Hamid Shojanazeri, Sophia Wen, Raghu Ganti, Geeta Chauhan
     
265. Scaling transformer to 1M tokens and beyond with RMT
     Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev
     
266. Self-instruct: aligning language model with self generated instructions
     Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
     
267. Self-normalizing neural networks
     Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
     
268. Semantically equivalent adversarial rules for debugging NLP models
     Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
     
269. Seq2seq model and the exposure bias problem
     Aditya Mohanty
     
270. Sequence parallelism: long sequence training from system perspective
     Shenggui Li, Fuzhao Xue, Chaitanya Baranwal, Yongbin Li, Yang You
     
271. Sequential latent knowledge selection for knowledge-grounded dialogue
     Byeongchang Kim, Jaewoo Ahn, Gunhee Kim
     
272. Simple and effective multi-paragraph reading comprehension
     Christopher Clark, Matt Gardner
     
273. Simplifying transformer blocks
     Bobby He, Thomas Hofmann
     
274. SmoothQuant: accurate and efficient post-training quantization for large language models
     Guangxuan Xiao, Ji Lin, Mickael Seznec, Julien Demouth, Song Han
     
275. Soft filter pruning for accelerating deep convolutional neural networks
     Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang
     
276. SOLAR 10.7B: scaling large language models with simple yet effective depth up-scaling
     Dahyun Kim, Chanjun Park, Sanghoon Kim, Wonsung Lee, Wonho Song, Yunsu Kim, Hyeonwoo Kim, Yungi Kim, Hyeonju Lee, Jihoo Kim, Changbae Ahn, Seonghoon Yang, Sukyung Lee, Hyunbyung Park, Gyoungjin Gim, Mikyoung Cha, Hwalsuk Lee, Sunghun Kim
     
277. SOLOIST: building task bots at scale with transfer learning and machine teaching
     Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Jianfeng Gao
     
278. Solving quantitative reasoning problems with language models
     Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
     
279. Spatial temporal graph convolutional networks for skeleton-based action recognition
     Sijie Yan, Yuanjun Xiong, Dahua Lin
     
280. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
     Bing Yu, Haoteng Yin, Zhanxing Zhu
     
281. Spectral normalization for generative adversarial networks
     Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
     
282. Speech and language processing
     Daniel Jurafsky, James H. Martin
     
283. StarCoder: may the source be with you!
     Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
     
284. Sticking the landing: simple, lower-variance gradient estimators for variational inference
     Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud
     
285. StitchNet: composing neural networks from pre-trained fragments
     Surat Teerapittayanon, Marcus Comiter, Brad McDanel, H.T. Kung
     
286. Stochastic hyperparameter optimization through hypernetworks
     Jonathan Lorraine, David Duvenaud
     
287. Strategies for teaching layered networks classification tasks
     Ben S. Wittner, John S. Denker
     
288. Structured prompting: scaling in-context learning to 1,000 examples
     Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, Furu Wei
     
289. Style transfer from non-parallel text by cross-alignment
     Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola
     
290. Subword regularization: improving neural network translation models with multiple subword candidates
     Taku Kudo
     
291. Supervised learning of probability distributions by neural networks
     Eric B. Baum, Frank Wilczek
     
292. Supporting efficient large model training on AMD Instinct^TM GPUs with DeepSpeed
     Olatunji Ruwase, Jeff Rasley
     
293. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity
     William Fedus, Barret Zoph, Noam Shazeer
     
294. Synchronization in neural nets
     Jacques J. Vidal, John Haggerty
     
295. Tackling the poor assumptions of Naive Bayes text classifiers
     Jason D. M. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger
     
296. The best of both worlds: combining recent advances in neural machine translation
     Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, Macduff Hughes
     
297. The elements of statistical learning: data mining, inference and prediction
     Trevor Hastie, Robert Tibshirani, Jerome Friedman
     
298. The Flan collection: designing data and methods for effective instruction tuning
     Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts
     
299. The information bottleneck method
     Naftali Tishby, Fernando C. Pereira, William Bialek
     
300. The Pile: an 800GB dataset of diverse text for language modeling
     Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy
     
301. The power of scale for parameter-efficient prompt tuning
     Brian Lester, Rami Al-Rfou, Noah Constant
     
302. The wisdom of hindsight makes language models better instruction followers
     Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez
     
303. Thermometer encoding: one hot way to resist adversarial examples
     Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow
     
304. To regularize or not to regularize? The bias variance trade-off in regularized AEs
     Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, Prathosh AP
     
305. Towards crowdsourced training of large neural networks using decentralized mixture-of-experts
     Max Ryabinin, Anton Gusev
     
306. Towards deep learning models resilient to adversarial attacks
     Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
     
307. Towards evaluating the robustness of neural networks
     Nicholas Carlini, David Wagner
     
308. Train short, test long: Attention with linear biases enables input length extrapolation
     Ofir Press, Noah Smith, Mike Lewis
     
309. Training compute-optimal large language models
     Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, Laurent Sifre
     
310. Training language models to follow instructions with human feedback
     Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe
     
311. Transformer memory as a differentiable search index
     Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler
     
312. Transformer quality in linear time
     Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc Le
     
313. Transformers explained visually (part 1): overview of functionality
     Ketan Doshi
     
314. Transformers explained visually (part 2): how it works, step-by-step
     Ketan Doshi
     
315. Transformers explained visually (part 3): multi-head attention, deep dive
     Ketan Doshi
     
316. Turing-NLG: a 17-billion-parameter language model by Microsoft
     Corby Rosset
     
317. UL2: unifying language learning paradigms
     Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler
     
318. Understanding convolutional neural networks with a mathematical model
     C.-C. Jay Kuo
     
319. Understanding disentangling in β-VAE
     Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner
     
320. Understanding the Open Pre-Trained Transformers (OPT) library
     Cameron Wolfe
     
321. Unit tests for stochastic optimization
     Tom Schaul, Ioannis Antonoglou, David Silver
     
322. Universal language model fine-tuning for text classification
     Jeremy Howard, Sebastian Ruder
     
323. Unlimiformer: long-range transformers with unlimited length input
     Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley
     
324. Unpaired image-to-image translation using cycle-consistent adversarial networks
     Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
     
325. Unsupervised machine translation using monolingual corpora only
     Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
     
326. Unsupervised representation learning by predicting image rotations
     Spyros Gidaris, Praveer Singh, Nikos Komodakis
     
327. Using DeepSpeed and Megatron to train Megatron-Turing NLG 530B, the world’s largest and most powerful generative language model
     Ali Alvi, Paresh Kharya
     
328. Variational inference using implicit distributions
     Ferenc Huszár
     
329. Variational inference with latent space quantization for adversarial resilience
     Vinay Kyatham, Mayank Mishra, Tarun Kumar Yadav, Deepak Mishra, Prathosh AP
     
330. Variational learning for unsupervised knowledge grounded dialogs
     Mayank Mishra, Dhiraj Madan, Gaurav Pandey, Danish Contractor
     
331. Variational lossy autoencoder
     Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
     
332. Vector-quantized input-contextualized soft prompts for natural language understanding
     Rishabh Bhardwaj, Amrita Saha, Steven C.H. Hoi, Soujanya Poria
     
333. VEEGAN: reducing mode collapse in GANs using implicit variational learning
     Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
     
334. Very deep convolutional networks for large-scale image recognition
     Karen Simonyan, Andrew Zisserman
     
335. Visualizing data using t-SNE
     Laurens van der Maaten, Geoffrey Hinton
     
336. Wasserstein GAN
     Martin Arjovsky, Soumith Chintala, Léon Bottou
     
337. wav2vec 2.0: a framework for self-supervised learning of speech representations
     Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
     
338. Wavenet: a generative model for raw audio
     Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu
     
339. WebGPT: browser-assisted question-answering with human feedback
     Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
     
340. What language model to train if you have one million GPU hours?
     Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy
     
341. Word translation without parallel data
     Guillaume Lample, Alexis Conneau, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
     
342. Yandex publishes YaLM 100B. It’s the largest GPT-like neural network in open source
     Mikhail Khrushchev
     
343. You only look once: unified, real-time object detection
     Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
     
344. ZeRO & DeepSpeed: new system optimizations enable training models with over 100 billion parameters
     DeepSpeed Team, Rangan Majumder, Junhua Wang
     
345. ZeRO++: Extremely efficient collective communication for giant model training
     Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Connor Holmes, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He
     
346. ZeRO-2 & DeepSpeed: shattering barriers of deep learning speed & scale
     DeepSpeed Team, Rangan Majumder, Junhua Wang
     
347. ZeRO-Infinity: breaking the GPU memory wall for extreme scale deep learning
     Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He
     
348. Zero-shot text-to-image generation
     Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
     
349. ZeRO: memory optimizations toward training trillion parameter models
     Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He
     
350. ZeroQuant: efficient and affordable post-training quantization for large-scale transformers
     Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He
     
351. β-VAE: learning basic visual concepts with a constrained variational framework
     Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
     

Calculus

1. Calculus of variations
   I. M. Gelfand, S. V. Fomin
   
2. Thomas' calculus
   George B. Thomas Jr., Maurice D. Weir
   

Computer Architecture

1. Accelerated computing with a reconfigurable dataflow architecture
   
2. Computer architecture: a quantitative approach
   John L. Hennessy, David A. Patterson
   
3. Computer organization and design ARM edition: the hardware software interface
   David A. Patterson, John L. Hennessy
   
4. Flipping bits in memory without accessing them: an experimental study of DRAM disturbance errors
   Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, Onur Mutlu
   
5. Improving DRAM performance by parallelizing refreshes with accesses
   Kevin Kai-Wei Chang, Donghyuk Lee, Zeshan Chishti, Alaa R. Alameldeen, Chris Wilkerson, Yoongu Kim, Onur Mutlu
   
6. Memory performance attacks: denial of memory service in multi-core systems
   Thomas Moscibroda, Onur Mutlu
   
7. Memory scaling: a systems architecture perspective
   Onur Mutlu
   
8. Millicode in an IBM zSeries processor
   L. C. Heller, M. S. Farrell
   
9. MTIA v1: Meta's first-generation AI inference accelerator
   Amin Firoozshahian, Olivia Wu, Joel Coburn, Roman Levenstein
   
10. RAIDR: Retention-Aware Intelligent DRAM Refresh
    Jamie Liu, Ben Jaiyen, Richard Veras, Onur Mutlu
    
11. Stall-time fair memory access scheduling for chip multiprocessors
    Onur Mutlu, Thomas Moscibroda
    

Computer Graphics

1. Principles of traditional animation applied to 3D computer animation
   John Lasseter
   

Data Structures and Algorithms

1. Data structures and algorithms in Java
   Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser
   
2. Introduction to algorithms
   Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein
   

Digital Electronics

1. Digital design: with an introduction to the Verilog HDL
   M. Morris Mano, Michael D. Ciletti
   

Graph Theory

1. Introduction to graph theory
   Robin Wilson
   

Information Theory

1. Elements of information theory
   Thomas M. Cover, Joy A. Thomas
   
2. Error detecting and error correcting codes
   R. W. Hamming
   

Linear Algebra

1. Linear algebra and its applications
   Gilbert Strang
   
2. Matrix analysis and applied linear algebra
   Carl D. Meyer
   
3. The matrix cookbook
   Kaare Brandt Petersen, Michael Syskind Pedersen
   

Measure Theory

1. Measure theory
   Donald L. Cohn
   

Optimization Theory

1. Convex Optimization
   Stephen Boyd, Lieven Vandenberghe
   
2. Distributed optimization and statistical learning via the alternating direction method of multipliers
   Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein
   

Probability and Stochastic Processes

1. Introduction to probability and stochastic processes with applications
   Liliana Blanco Castaneda, Viswanathan Arunachalam, Delvamuthu Dharmaraja
   

Quantum Computing

1. A fast quantum mechanical algorithm for database search
   Lov K. Grover
   
2. A single quantum cannot be cloned
   W. K. Wootters, W. H. Zurek
   
3. Can quantum-mechanical description of physical reality be considered complete
   Albert Einstein, Boris Podolsky, Nathan Rosen
   
4. Image recognition with an adiabatic quantum computer I. mapping to quadratic unconstrained binary optimization
   Hartmut Neven, Geordie Rose, William G. Macready
   
5. Integer optimization toolbox (minimizing polynomials over integer lattices using quantum annealing)
   Pooya Ronagh
   
6. Limits on parallel speedup for classical Ising model solvers
   
7. Partitioning optimization problems for hybrid classical/quantum execution
   Michael Booth, Steven P. Reinhardt, Aidan Roy
   
8. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
   Peter W. Shor
   
9. Probabilistic cloning and identification of linearly independent quantum states
   Lu-Ming Duan, Guang-Can Guo
   
10. Programming with D-Wave: map coloring problem
    E. D. Dahl
    
11. Quantum computation and quantum information
    Michael A. Nielsen, Isaac L. Chuang
    
12. Quantum computing: a gentle introduction
    Eleanor Rieffel, Wolfgang Polak
    
13. Quantum performance evaluation: a short reading list
    
14. Quantum theory, the Church-Turing principle and the universal quantum computer
    David Deutsch
    
15. Rapid solution of problems by quantum computation
    David Deutsche, Richard Jozsa
    
16. Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels
    Charles H. Bennett, Gilles Brassard, Claude Crépeau, Richard Jozsa, Asher Peres, William K. Wootters
    

Signal Processing

1. Discrete-time signal processing
   Alan V. Oppenheim, Ronald W. Schafer
   
2. Foundations of Signal Processing
   Martin Vetterli, Jelena Kovačević, Vivek K Goyal
   
3. Signals and systems
   Alan V. Oppenheim
   
4. Understanding digital signal processing
   Richard G. Lyons