đź“– A curated list of resources dedicated to Machine Learning for Systems research
- A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units. Adi Szeskin, Lev Faivishevsky, Ashwin K. Muppalla, Amitai Armon, and Tom Hope
- CodeCaption: A dataset for captioning data science code. Ioana Baldini, Kavitha Srinivas, and Jiri Navratil
- Defeating the Curse of Dimensionality to Scale JIT Fusion. Jonathan Raiman
- Learning Multi-dimensional Indexing. Vikram Nathan, Jialin Ding, Mohammad Alizadeh, and Tim Kraska
- Learned TPU Cost Model for XLA Tensor Programs. Samuel J. Kaufman, Phitchaya Phothilimtha, and Mike Burrows
- Learning Caching Policies with Subsampling. Haonan Wang, Hao He, Mohammad Alizadeh, and Hongzi Mao
- Learning to Fuse. Amirali Abdolrashidi, Qiumin Xu, Shibo Wang, Sudip Roy, and Yanqi Zhou
- SOSD: A Benchmark for Learned Indexes. Andreas Kipf, Ryan Marcus, Alexander van Renen*, Mihail Stoian, Alfons Kemper, Tim Kraska, and Thomas Neumann
- Learning to Vectorize Using Deep Reinforcement Learning. Ameer Haj-Ali, Nesreen Ahmed, Theodore L. Willke, Yakun Sophia Shao, Krste Asanovic, and Ion Stoica
- MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. Viswanath Sivakumar, Tim Rocktäschel, Alexander Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, and Sebastian Riedel
- Multi-Task Learning for Storage Systems. Giulio Zhou, and Martin Maas
- Neural Hierarchical Sequence Model for Irregular Data Prefetching. Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, and Calvin Lin
- Neural-Hardware Architecture Search. Yujun Lin, Driss Hafdi, Kuan Wang, Zhijian Liu, and Song Han
- PRIC: A Privacy-Respecting Image Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations. Ang Li, Yixiao Duan, Huanrui Yang, Yiran Chen, and Jianlei Yang
- Predictive Precompute with Recurrent Neural Networks. Hanson Wang, Zehui Wang, and Yuanyuan Ma
- QoS-aware Neural Architecture Search. An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, and Min Sun
- Real-time Policy Distillation in Deep Reinforcement Learning. Yuxiang Sun, and Pooyan Fazli
- Reinforcement Learning guided Software Debloating. Nham V Le, Ashish Gehani, Arie Gurfinkel, Susmit Jha, and Jorge A. Navas!
- Reinforcement learning for bandwidth estimation and congestion control in real-time communications. Joyce Fang, Martin Ellis, Bin Li, Siyao Liu, Yasaman Hosseinkashi, Michael Revow, Albert Sadovnikov, Ziyuan Liu, Peng Cheng, Sachin Ashok, David Zhao, Ross Cutler, Yan Lu, and Johannes Gehrke
- Towards Safe Online Reinforcement Learning in Computer Systems. Hongzi Mao, Malte Schwarzkopf, Hao He, and Mohammad Alizadeh
- TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing. Vinoj Jayasundara, Nghi Bui, Lingxiao Jiang, and David Lo
- Zero-Shot Learning for Fast Optimization of Computation Graphs. Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, and Oriol Vinyals
- SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training. Ahmed T. Elthakeb, Prannoy Pilligundla, and Hadi Esmaeilzadeh
- Learning automatic schedulers with projective reparameterization. Ajay Jain, and Saman Amarasingh
- Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin
- AutoRank: Automated Rank Selection for Effective Neural Network Customization. Mohammad Samragh, Mojan Javaheripi, and Farinaz Koushanfar
- Optimal Learning-Based Network Protocol Selection. Xiaoxi Zhang, Siqi Chen, Youngbin Im, Maria Gorlotova, Sangtae Ha, and Carlee Joe-Wong
- Learning Scheduling Algorithms for Data Processing Clusters. Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh
- Generative Adversarial Networks for Clustering Semiconductor Wafer Maps. Hamidreza Mahyar, Elahe Ghalebi, Peter Tulala, and Radu Grusu
- Virtual Address Translation via Learned Page Table Indexes. Artemiy Margaritov, Dmitri Ustiugov, Edouard Bugnion, and Boris Grot
- Exploring the Use of Learning Algorithms for Efficient Performance Profiling. Shoumik Palkar*, Sahaana Suri*, Matei Zaharia, and Peter D. Bailis
- Neural Inference of API Functions from Input–Output Examples. Rohan Bavishi, Caroline Lemieux, Neel Kant, Roy Fox, Koushik Sen, and Ion Stoica.
- A K-means Cluster-Driven Calibration to Improve the Accuracy of Personal Wearable UV Sensors. Thomas Pumir, Emmanuel Dumont, Peter Kaplan, and Shayak Banerjee
- DeepConf: Automating Data Center Network Topologies Management with Machine Learning. Saim Salman, Theophilus Benson, and Asim Kadav
- Cache Miss Rate Predictability via Neural Networks. Rishikesh Jha*, Arjun Karuvally*, Saket Tiwari*, and J. Eliot B. Moss
- Placeto: Efficient Progressive Device Placement Optimization. Ravichandra Addanki, Shaileshh Venkatakrishnan, Shreyan Gupta, Hongzi Mao, and Dr. Mohammad Alizadeh
- Lifting the Curse of Multidimensional Data with Learned Existence Indexes. Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, and Ed H. Chi
- PeCC: Prediction-error Correcting Cache. Vaishnav Janardhan and Adit Bhardwaj
- Chasing the Signal: Statistically Separating Multi-Tenant I/O Workloads. Si Chen and Avani Wildani
- Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control. Fabian Ruffy*, Michael Przystupa*, and Ivan Beschastnikh
- Learning to Optimize Tensor Programs. Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy
- Learning to Design Circuits. Hanrui Wang*, Jiacheng Yang*, Hae-Seung Lee, and Song Han
- End-to-end Learning for Distributed Circuit Design. Hao He*, Guo Zhang*, Jack Holloway, and Dina Katabi
- Dali: Lazy Compilation & Kernel Fusion in Dynamic Computation Graphs. Jonathan Raiman
- ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks. Amir Yazdanbakhsh*, Ahmed T. Elthakeb*, Prannoy Pilligundla, Fatemeh Sadat Mireshghallah, and Hadi Esmaeilzadeh
- Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. Lev Faivishevsky, Ashwin K Muppalla, Ravid Shwartz-Ziv, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, and Amitai Armon
- Neo: A Learned Query Optimizer. Ryan Marcus and Parimarjan Negi and Hongzi Mao and Chi Zhang and Mohammad Alizadeh and Tim Kraska and Olga Papaemmanouil and Nesime Tatbul. PVLDB 2019
- SageDB: A Learned Database System. Tim Kraska and Mohammad Alizadeh and Alex Beutel and Ed H. Chi and Ani Kristo and Guillaume Leclerc and Samuel Madden and Hongzi Mao and Vikram Nathan. CIDR 2019
- FITing-Tree: A Data-aware Index Structure. Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska. SIGMOD 2019
- Sherlock: A Deep Learning Approach to Semantic Data Type Detection. Adelon Hulsebos and Kevin Zeng Hu and Michiel A. Bakker and Emanuel Zgraggen and Arvind Satyanarayan and Tim Kraska and Cagatay Demiralp and CĂ©sar A. Hidalgo. KDD 2019
- From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems (Tutorial). Stratos Idreos and Tim Kraska. SIGMOD 2019
- Park: An Open Platform for Learning-Augmented Computer Systems. Hongzi Mao and Parimarjan Negi and Akshay Narayan and Hanrui Wang and Jiacheng Yang and Haonan Wang and Ryan Marcus and Ravichandra Addanki and Mehrdad Khani Shirkoohi and Songtao He and Vikram Nathan and Frank Cangialosi and Shaileshh Venkatakrishnan and Wei-Hung Weng and Song Han and Tim Kraska and Dr.Mohammad Alizadeh. NeurIPS 2019
- The Case for a Learned Sorting Algorithm. Ani Kristo and Kapil Vaidya and Ugur Cetintemel and Tim Kraska. AI Systems workshop at SOSP 2019
- SysML: The New Frontier of Machine Learning Systems. Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub KoneÄŤnĂ˝, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher RĂ©, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
- The Case for Learned Index Structures. Tim Kraska and Alex Beutel and Ed H. Chi and Jeffrey Dean and Neoklis Polyzotis. SIGMOD 2018
- Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?. Christopher RĂ© and Divy Agrawal and Magdalena Balazinska and Michael I. Cafarella and Michael I. Jordan and Tim Kraska and Raghu Ramakrishnan. SIGMOD 2015