ixn-xhio/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
   
2. 5 best practices for efficient model training
   
3. 8-bit approximations for parallelism in deep learning
   
4. 8-bit optimizers via block-wise quantization
   
5. A 'neural' network that learns to play Backgammon
   
6. A BetterTransformer for fast transformer inference
   
7. A deep reinforced model for abstractive summarization
   
8. A dynamical approach to temporal pattern processing
   
9. A few more examples may be worth billions of parameters
   
10. A general and adaptive robust loss function
    
11. A generalist agent
    
12. A gentle introduction to 8-bit matrix multiplication for transformers at scale using Hugging Face transformers, accelerate and bitsandbytes
    
13. A note on the evaluation of generative models
    
14. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
    
15. A simple but tough-to-beat baseline for sentence embeddings
    
16. A simple language model for task-oriented dialogue
    
17. A simple neural attentive meta-learner
    
18. A simple neural network module for relational reasoning
    
19. A study of BFLOAT16 for deep learning training
    
20. A style-based generator architecture for generative adversarial networks
    
21. A stylometric inquiry into hyperpartisan and fake news
    
22. A3T: adversarially augmented adversarial training
    
23. Accelerated PyTorch 2 transformers
    
24. Accelerating large language model training with variable sparse pre-training and dense fine-tuning
    
25. Accelerating PyTorch with CUDA graphs
    
26. AdapterHub: a framework for adapting transformers
    
27. Adversarial approximate inference for speech to electroglottograph conversion
    
28. Adversarial autoencoders
    
29. Adversarial examples that fool both computer vision and time-limited humans
    
30. Adversarial feature learning
    
31. Adversarial generation of natural language
    
32. Adversarial information factorization
    
33. Adversarially learned inference
    
34. AlexaTM 20B: few-shot learning using a large-scale multilingual seq2seq model
    
35. Amazon SageMaker model parallelism: a general and flexible framework for large model training
    
36. An image is worth 16x16 words: transformers for image recognition at scale
    
37. An overview of gradient descent optimization algorithms
    
38. Analysing mathematical reasoning abilities of neural models
    
39. Approximation by superpositions of sigmoidal function
    
40. Artificial Intelligence: a modern approach
    
41. Aspect based sentiment analysis with gated convolutional networks
    
42. Attention is all you need
    
43. Attention is off by one
    
44. Auto-encoding variational Bayes
    
45. Backpropagation through the void: optimizing control variates for black-box gradient estimation
    
46. BART: denoising sequence-to-sequence pre-training for natural language generation, translation and comprehension
    
47. Batch normalization: accelerating deep network training by reducing internal covariate shift
    
48. Behavioral cloning from observation
    
49. BERT: pre-training of deep bidirectional transformers for language understanding
    
50. Beyond domain APIs: Task-oriented conversational modeling with unstructured knowledge access
    
51. BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation
    
52. Blockwise parallel transformer for large context models
    
53. BLOOM: A 176B-parameter open-access multilingual language model
    
54. Bootstrapping entity alignment with knowledge graph embedding
    
55. Bridging the gap between prior and posterior knowledge selection for knowledge-grounded dialogue generation
    
56. Bringing open large language models to consumer devices
    
57. BTLM-3B-8K: 7B performance in a 3 billion parameter model
    
58. Building blocks for a complex-valued transformer architecture
    
59. CATS: contextually-aware thresholding for sparsity in large language models
    
60. ChatGPT: optimizing language models for dialogue
    
61. ColBERT: efficient and effective passage search via contextualized late interaction over BERT
    
62. Colossal-AI: a unified deep learning system for large-scale parallel training
    
63. Compiling machine learning programs via high-level tracing
    
64. Complex transformer: a framework for modeling complex-valued sequence
    
65. Conceptual captions: a cleaned, hypernymed, image alt-text dataset for automatic image captioning
    
66. Conditional image synthesis with auxilliary classifier GANs
    
67. Conformal nucleus sampling
    
68. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
    
69. Connectivity versus entropy
    
70. Constituency parsing with a self-attentive encoder
    
71. Constraint based knowledge base distillation in end-to-end task oriented dialogs
    
72. Context generation improves open domain question answering
    
73. Convert transformers to ONNX with hugging face optimum
    
74. Convolutional networks for graphs for learning molecular fingerprints
    
75. Convolutional neural network language models
    
76. Countering adversarial images using input transformations
    
77. Cramming: training a language model on a single GPU in one day
    
78. Crosslingual generalization through multitask finetuning
    
79. Curriculum learning
    
80. Cutting down on prompts and parameters: simple few-shot learning with language models
    
81. Data engineering for scaling language models to 128K context
    
82. Deep Boltzmann machines
    
83. Deep complex networks
    
84. Deep learning
    
85. Deep learning and the information bottleneck principle
    
86. Deep learning techniques for super-resolution in video games
    
87. Deep residual learning for image recognition
    
88. Deep text classification can be fooled
    
89. DeepSpeed compression: a composable library for extreme compression and zero-cost quantization
    
90. DeepSpeed Inference: enabling efficient inference of transformer models at unprecedented scale
    
91. DeepSpeed powers 8x larger MoE model training with high performance
    
92. DeepSpeed Ulysses: system optimizations for enabling training of extreme long sequence transformer models
    
93. DeepSpeed: accelerating large-scale model inference and training via system optimizations and compression
    
94. DeepSpeed: advancing MoE inference and training to power next-generation AI scale
    
95. Denoising distantly supervised open-domain question answering
    
96. Diffusion convolutional recurrent neural network: data-driven traffic forecasting
    
97. Discrete variational autoencoders
    
98. Disentangling by factorising
    
99. Disentangling language and knowledge in task-oriented dialogs
    
100. Distributionally robust language modeling
     
101. Editing models with task arithmetic
     
102. Efficient estimation of word representations in vector space
     
103. Efficient large scale language modeling with mixtures of experts
     
104. Efficient large-scale language model training on GPU clusters using Megatron-LM
     
105. Enchancing the reliability of out-of-distribution image detection in neural networks
     
106. End-to-end task-oriented dialog modeling with semi-structured knowledge management
     
107. Enhance reasoning for large language models in the game Werewolf
     
108. Ensemble adversarial training: attacks and defenses
     
109. Equilibrium propagation: bridging the gap between energy-based models and backpropagation
     
110. Estimating or propagating gradients through stochastic neurons for conditional computation
     
111. Exemplar encoder-decoder for neural conversation generation
     
112. Expert human-level driving in gran turismo sport using deep reinforcement learning with image-based representation
     
113. Exploring deep recurrent models with reinforcement learning for molecule design
     
114. Exploring the limits of transfer learning with a unified text-to-text transformer
     
115. Extreme compression for pre-trained transformers made simple and efficient
     
116. Fast abstractive summarization with reinforce-selected sentence rewriting
     
117. Fast benchmarking of accuracy vs. training time with cyclic learning rates
     
118. Fast transformer decoding: one write-head is all you need
     
119. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
     
120. FFJORD: Free-form continuous dynamics for scalable reversible generative models
     
121. Finetuned language models are zero-shot learners
     
122. Flash-decoding for long-context inference
     
123. FlashAttention: fast and memory-efficient exact attention with IO-awareness
     
124. FlashAttention: fast transformer training with long sequences
     
125. Foundations of NLP explained visually: beam search, how it works
     
126. FP8 formats for deep learning
     
127. FP8-LM: training FP8 large language models
     
128. Gemini: a family of highly capable multimodal models
     
129. Gemma: open models based on Gemini research and technology
     
130. Generating adversarial examples with adversarial networks
     
131. Generating sentences from a continuous space
     
132. Generation-augmented retrieval for open-domain question answering
     
133. Generative adversarial nets
     
134. Generative pretraining from pixels
     
135. Genetic algorithms in search, optimization and machine learning
     
136. GeoMAN: multi-level attention networks for geo-sensory time series prediction
     
137. Getting the most out of the NVIDIA A100 GPU with Multi-Instance GPU
     
138. GLaM: efficient scaling of language models with mixture-of-experts
     
139. GLM-130B: an open bilingual pre-trained model
     
140. GLU variants improve transformer
     
141. Going deeper with convolutions
     
142. GPT-4 architecture, infrastructure, training dataset, costs, vision, MoE
     
143. GPT-NeoX-20B: an open-source autoregressive language model
     
144. GQA: training generalized multi-query transformer models from multi-head checkpoints
     
145. Gradient-based hyperparameter optimization through reversible learning
     
146. Graph attention networks
     
147. Grounding large language models in interactive environments with online reinforcement learning
     
148. Hierarchical neural story generation
     
149. Hindsight: posterior-guided training of retrievers for improved open-ended generation
     
150. HotFlip: white-box adversarial examples for text classification
     
151. How big should my language model be?
     
152. How Pytorch 2.0 accelerates deep learning with operator fusion and CPU/GPU code-generation
     
153. How should AI systems behave, and who should decide?
     
154. How we sped up transformer inference 100x for 🤗 API customers
     
155. How 🤗 Accelerate runs very large models thanks to PyTorch
     
156. Hydragen: high-throughput LLM inference with shared prefixes
     
157. HyKnow: end-to-end task-oriented dialog modeling with hybrid knowledge management
     
158. Hyperparameter search with Transformers and Ray Tune
     
159. Image-to-image translation with conditional generative adversarial networks
     
160. ImageNet classification using deep convolutional neural networks
     
161. Improving entity linking by modeling latent relations between mentions
     
162. Improving language models by retrieving from trillions of tokens
     
163. Improving language understanding by generative pre-training
     
164. Improving reinforcement learning from human feedback with efficient reward model ensemble
     
165. Incredibly fast BLOOM inference with DeepSpeed and Accelerate
     
166. Inference suboptimality in variational autoencoders
     
167. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets
     
168. Interpretable convolutional neural networks via feedforward design
     
169. Introducing MPT-7B: a new standard for open-source, commercially usable LLMs
     
170. Introducing nvFuser, a deep learning compiler for PyTorch
     
171. Introducing Turing image super resolution: AI powered image enhancements for Microsoft Edge and Bing maps
     
172. Introducing 🤗 accelerate
     
173. Is ChatGPT 175 billion parameters? Technical analysis
     
174. Is the future of neural networks Sparse? An introduction (1/N)
     
175. Jack of all trades, master of some, a multi-purpose transformer agent
     
176. Jack of all trades, master of some, a multi-purpose transformer agent
     
177. Joint reasoning on hybrid-knowledge sources for task-oriented dialog
     
178. Judging LLM-as-a-judge with MT-bench and chatbot arena
     
179. Know what you don't know: unanswerable questions for SQuAD
     
180. Knowledge-grounded dialogue generation with pre-trained language models
     
181. Language is not all you need: aligning perception with language models
     
182. Language modeling with gated convolutional networks
     
183. Language modelling with pixels
     
184. Language models (mostly) know what they know
     
185. Language models are unsupervised multitask learners
     
186. Language models as compilers: simulating pseudocode execution improves algorithmic reasoning in language models
     
187. Large language models are not fair evaluators
     
188. Layer normalization
     
189. Learning activation functions to improve deep neural networks
     
190. Learning associative inference using fast weight memory
     
191. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
     
192. Learning on a general network
     
193. Learning representations by back-propagating errors
     
194. Learning transferable visual models from natural language supervision
     
195. Learning transferable visual models from natural language supervision
     
196. Learning word embeddings efficiently with noise-contrastive estimation
     
197. Leave no context behind: efficient infinite context transformers with infini-attention
     
198. Lessons learned on language model safety and misuse
     
199. Lifelong language pretraining with distribution-specialized experts
     
200. Linear scaling made possible with weight streaming
     
201. Linformer: self-attention with linear complexity
     
202. LLM in a flash: efficient large language model inference with limited memory
     
203. LLM.int8(): 8-bit matrix multiplication for transformers at scale
     
204. Long sequence modeling with XGen: a 7B LLM trained on 8K input sequence length
     
205. LoRA: Low-Rank Adaptation of large language models
     
206. Lost in the middle: how language models use long contexts
     
207. M6-10T: a sharing-delinking paradigm for efficient multi-trillion parameter pretraining
     
208. Machine learning
     
209. Machine learning: a probabilistic perspective
     
210. Making deep learning go brrrr from first principles
     
211. Making DeepSpeed ZeRO run efficiently on more-affordable hardware
     
212. Mask & focus: conversation modelling by learning concepts
     
213. Matryoshka representation learning
     
214. Maximizing communication efficiency for large-scale training via 0/1 Adam
     
215. MCR-DL: mix-and-match communication runtime for deep learning
     
216. MegaBlocks: efficient sparse training with mixture-of-experts
     
217. Megatron-LM: training multi-billion parameter language models using model parallelism
     
218. Memory-efficient pipeline-parallel DNN training
     
219. MinTL: minimalist transfer learning for task-oriented dialogue systems
     
220. Mix and match: learning-free controllable text generation using energy language models
     
221. Mixed precision training
     
222. Mixture of attention heads: selecting attention heads per token
     
223. Mixture-of-Experts meets instruction tuning: a winning combination for large language models
     
224. mixup: beyond empirical risk minimization
     
225. MMCoQA: conversational question answering over text, tables and images
     
226. Mode matching in GANs through latent space learning and inversion
     
227. Multi-level memory for task oriented dialogs
     
228. Multitask prompt tuning enables parameter-efficient transfer learning
     
229. MultiWOZ - A large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling
     
230. Mutual information neural estimation
     
231. NeMo: a toolkit for building AI applications using neural modules
     
232. Neural GPUs learn algorithms
     
233. Neural network methods for natural language processing
     
234. Neural networks and physical systems with emergent collective computational abilities
     
235. Neural networks for pattern recognition
     
236. Neural ordinary differential equations
     
237. No train no gain: revisiting efficient training algorithms for transformer-based language models
     
238. Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples
     
239. OctoPack: instruction tuning code large language models
     
240. On the convergence of Adam and beyond
     
241. On the power of neural networks for solving hard problems
     
242. One model to learn them all
     
243. Open domain question answering over tables via dense retrieval
     
244. Open question answering over tables and text
     
245. OPT: open pre-trained transformer language models
     
246. Optimal brain compression: a framework for accurate post-training quantization and pruning
     
247. Optimal perceptual inference
     
248. Optimization story: Bloom inference
     
249. Orca 2: teaching small language models how to reason
     
250. Orca: progressive learning from complex explanation traces of GPT-4
     
251. Outer product-based neural collaborative filtering
     
252. Outrageously large neural networks: the sparsely-gated mixture-of-experts layer
     
253. Overcoming oscillations in quantization-aware training
     
254. PAL: Program-aided language models
     
255. PaLM: scaling language modeling with pathways
     
256. Parallel context windows improve in-context learning of large language models
     
257. Pattern classification
     
258. Pattern recognition and machine learning
     
259. Perceptual losses for real-time style transfer and super-resolution
     
260. Personalizing dialogue agents: I have a dog, do you have pets too?
     
261. Phase-functioned neural networks for character control
     
262. Playing Atari with deep reinforcement learning
     
263. Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing
     
264. Prefix-tuning: optimizing continuous prompts for generation
     
265. Probabilistic latent semantic analysis
     
266. Progressive growing of GANs from improved quality, stability and variation
     
267. Prompting with pseudo-code instructions
     
268. Proximal policy optimization algorithms
     
269. PullNet: open domain question answering with iterative retrieval on knowledge bases and text
     
270. PyTorch trace analysis for the masses
     
271. Q-BERT: Hessian based ultra low precision quantization of BERT
     
272. R³Net: recurrent residual refinement network for saliency detection
     
273. Reading Wikipedia to answer open-domain questions
     
274. REALM: Retrieval-augmented language model pretraining
     
275. Recurrent models of visual attention
     
276. Reducing activation recomputation in large transformer models
     
277. Regularizing and optimizing LSTM language models
     
278. Reinforcement Learning: An Introduction
     
279. ReLoRA: high-rank training through low-rank updates
     
280. Restricted Boltzmann machines for collaborative filtering
     
281. Retrieval augmentation reduces hallucination in conversation
     
282. Retrieval-augmented generation for knowledge-intensive NLP tasks
     
283. Revisiting classifier two-sample tests
     
284. RoBERTa: a robustly optimized BERT pretraining approach
     
285. RoFormer: enhanced transformer with rotary position embedding
     
286. SantaCoder: don't reach for the stars!
     
287. Scaling instruction-finetuned language models
     
288. Scaling PyTorch FSDP for training foundation Models on IBM cloud
     
289. Scaling transformer to 1M tokens and beyond with RMT
     
290. Self-instruct: aligning language model with self generated instructions
     
291. Self-normalizing neural networks
     
292. Semantically equivalent adversarial rules for debugging NLP models
     
293. Seq2seq model and the exposure bias problem
     
294. Sequence parallelism: long sequence training from system perspective
     
295. Sequential latent knowledge selection for knowledge-grounded dialogue
     
296. Simple and effective multi-paragraph reading comprehension
     
297. Simplifying transformer blocks
     
298. SlimPajama-DC: understanding data combinations for LLM training
     
299. SmoothQuant: accurate and efficient post-training quantization for large language models
     
300. Soft filter pruning for accelerating deep convolutional neural networks
     
301. SOLAR 10.7B: scaling large language models with simple yet effective depth up-scaling
     
302. SOLOIST: building task bots at scale with transfer learning and machine teaching
     
303. Solving quantitative reasoning problems with language models
     
304. Spatial temporal graph convolutional networks for skeleton-based action recognition
     
305. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
     
306. Spectral normalization for generative adversarial networks
     
307. Speech and language processing
     
308. StarCoder: may the source be with you!
     
309. Sticking the landing: simple, lower-variance gradient estimators for variational inference
     
310. StitchNet: composing neural networks from pre-trained fragments
     
311. Stochastic hyperparameter optimization through hypernetworks
     
312. Strategies for teaching layered networks classification tasks
     
313. Structured prompting: scaling in-context learning to 1,000 examples
     
314. Style transfer from non-parallel text by cross-alignment
     
315. Subword regularization: improving neural network translation models with multiple subword candidates
     
316. Supervised learning of probability distributions by neural networks
     
317. Supporting efficient large model training on AMD Instinct^TM GPUs with DeepSpeed
     
318. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity
     
319. Synchronization in neural nets
     
320. Synthetic data (almost) from scratch: generalized instruction tuning for language models
     
321. Tackling the poor assumptions of Naive Bayes text classifiers
     
322. Tensor programs V: tuning large neural networks via zero-shot hyperparameter transfer
     
323. TextWorld: a learning environment for text-based games
     
324. The best of both worlds: combining recent advances in neural machine translation
     
325. The elements of statistical learning: data mining, inference and prediction
     
326. The Flan collection: designing data and methods for effective instruction tuning
     
327. The information bottleneck method
     
328. The Pile: an 800GB dataset of diverse text for language modeling
     
329. The power of scale for parameter-efficient prompt tuning
     
330. The wisdom of hindsight makes language models better instruction followers
     
331. Thermometer encoding: one hot way to resist adversarial examples
     
332. To regularize or not to regularize? The bias variance trade-off in regularized AEs
     
333. Towards crowdsourced training of large neural networks using decentralized mixture-of-experts
     
334. Towards deep learning models resilient to adversarial attacks
     
335. Towards evaluating the robustness of neural networks
     
336. Train short, test long: Attention with linear biases enables input length extrapolation
     
337. Training compute-optimal large language models
     
338. Training language models to follow instructions with human feedback
     
339. Transformer memory as a differentiable search index
     
340. Transformer quality in linear time
     
341. Transformer-XL: attentive language models beyond a fixed-length context
     
342. Transformers explained visually (part 1): overview of functionality
     
343. Transformers explained visually (part 2): how it works, step-by-step
     
344. Transformers explained visually (part 3): multi-head attention, deep dive
     
345. Turing-NLG: a 17-billion-parameter language model by Microsoft
     
346. UL2: unifying language learning paradigms
     
347. Understanding convolutional neural networks with a mathematical model
     
348. Understanding disentangling in β-VAE
     
349. Understanding the Open Pre-Trained Transformers (OPT) library
     
350. Unit tests for stochastic optimization
     
351. Universal language model fine-tuning for text classification
     
352. Unlimiformer: long-range transformers with unlimited length input
     
353. Unpaired image-to-image translation using cycle-consistent adversarial networks
     
354. Unsupervised machine translation using monolingual corpora only
     
355. Unsupervised representation learning by predicting image rotations
     
356. Using DeepSpeed and Megatron to train Megatron-Turing NLG 530B, the world’s largest and most powerful generative language model
     
357. Variational inference using implicit distributions
     
358. Variational inference with latent space quantization for adversarial resilience
     
359. Variational learning for unsupervised knowledge grounded dialogs
     
360. Variational lossy autoencoder
     
361. Vector-quantized input-contextualized soft prompts for natural language understanding
     
362. VEEGAN: reducing mode collapse in GANs using implicit variational learning
     
363. Very deep convolutional networks for large-scale image recognition
     
364. Visual instruction tuning
     
365. Visualizing data using t-SNE
     
366. Wasserstein GAN
     
367. wav2vec 2.0: a framework for self-supervised learning of speech representations
     
368. Wavenet: a generative model for raw audio
     
369. WebGPT: browser-assisted question-answering with human feedback
     
370. What language model to train if you have one million GPU hours?
     
371. Will GPT-4 run DOOM?
     
372. Word translation without parallel data
     
373. Yandex publishes YaLM 100B. It’s the largest GPT-like neural network in open source
     
374. You only cache once: decoder-decoder architectures for language models
     
375. You only look once: unified, real-time object detection
     
376. ZeRO & DeepSpeed: new system optimizations enable training models with over 100 billion parameters
     
377. ZeRO++: Extremely efficient collective communication for giant model training
     
378. ZeRO-2 & DeepSpeed: shattering barriers of deep learning speed & scale
     
379. ZeRO-Infinity: breaking the GPU memory wall for extreme scale deep learning
     
380. Zero-shot text-to-image generation
     
381. ZeRO: memory optimizations toward training trillion parameter models
     
382. ZeroQuant: efficient and affordable post-training quantization for large-scale transformers
     
383. β-VAE: learning basic visual concepts with a constrained variational framework
     

Calculus

1. Calculus of variations
   
2. Thomas' calculus
   

Computer Architecture

1. Accelerated computing with a reconfigurable dataflow architecture
   
2. Computer architecture: a quantitative approach
   
3. Computer organization and design ARM edition: the hardware software interface
   
4. Flipping bits in memory without accessing them: an experimental study of DRAM disturbance errors
   
5. Improving DRAM performance by parallelizing refreshes with accesses
   
6. Memory performance attacks: denial of memory service in multi-core systems
   
7. Memory scaling: a systems architecture perspective
   
8. Millicode in an IBM zSeries processor
   
9. MTIA v1: Meta's first-generation AI inference accelerator
   
10. RAIDR: Retention-Aware Intelligent DRAM Refresh
    
11. Stall-time fair memory access scheduling for chip multiprocessors
    

Computer Graphics

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

Data Structures and Algorithms

1. Data structures and algorithms in Java
   
2. Introduction to algorithms
   

Digital Electronics

1. Digital design: with an introduction to the Verilog HDL
   

Graph Theory

1. Introduction to graph theory
   

Information Theory

1. Elements of information theory
   
2. Error detecting and error correcting codes
   

Linear Algebra

1. Linear algebra and its applications
   
2. Matrix analysis and applied linear algebra
   
3. The matrix cookbook
   

Measure Theory

1. Measure theory
   

Optimization Theory

1. Convex Optimization
   
2. Distributed optimization and statistical learning via the alternating direction method of multipliers
   

Probability and Stochastic Processes

1. Introduction to probability and stochastic processes with applications
   

Quantum Computing

1. A fast quantum mechanical algorithm for database search
   ![image][Paper] ![image][Quantum Algorithms] ![image][Quantum Computing]
2. A single quantum cannot be cloned
   ![image][Paper] ![image][Quantum Computing]
3. Can quantum-mechanical description of physical reality be considered complete
   ![image][Paper] ![image][Quantum Computing]
4. Image recognition with an adiabatic quantum computer I. mapping to quadratic unconstrained binary optimization
   ![image][Paper] ![image][Image Classification] ![image][QUBO] ![image][Quantum Computing]
5. Integer optimization toolbox (minimizing polynomials over integer lattices using quantum annealing)
   ![image][Whitepaper]
6. Limits on parallel speedup for classical Ising model solvers
   ![image][Whitepaper]
7. Partitioning optimization problems for hybrid classical/quantum execution
   ![image][Whitepaper]
8. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
   ![image][Paper] ![image][Quantum Algorithms] ![image][Quantum Computing]
9. Probabilistic cloning and identification of linearly independent quantum states
   ![image][Paper] ![image][Cloning] ![image][Quantum Computing]
10. Programming with D-Wave: map coloring problem
    ![image][Whitepaper]
11. Quantum computation and quantum information
    ![image][Book]
12. Quantum computing: a gentle introduction
    ![image][Book]
13. Quantum performance evaluation: a short reading list
    ![image][Whitepaper]
14. Quantum theory, the Church-Turing principle and the universal quantum computer
    ![image][Paper] ![image][Quantum Computing] ![image][Theory of Computation]
15. Rapid solution of problems by quantum computation
    ![image][Paper] ![image][Quantum Algorithms] ![image][Quantum Computing]
16. Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels
    ![image][Paper] ![image][Quantum Computing] ![image][Quantum Teleportation]

Signal Processing

1. Discrete-time signal processing
   ![image][Book]
2. Foundations of Signal Processing
   ![image][Book]
3. Signals and systems
   ![image][Book]
4. Understanding digital signal processing
   ![image][Book]