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selected_papers
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# selected papers ### Machine Learning - M. Jordan et al., **An Introduction to MCMC for Machine Learning**, Machine Learning, 2003 [[pdf]](http://www.cs.bham.ac.uk/~axk/mcmc1.pdf) - Y. Teh et al. **Hierarchical Dirichlet Processes**, Journal of American Statistical Association, 2006 [[pdf]](https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf) - A. Patil et al. **PyMC: Bayesian Stochastic Modelling in Python**, Journal of Statistical Software, 2010 [[pdf]](https://www.jstatsoft.org/article/view/v035i04) - D. Lin et al. **Construction of Dependent Dirichlet Processes based on Poisson Processes**, NeurIPS, 2010 [[pdf]](https://papers.nips.cc/paper/4151-construction-of-dependent-dirichlet-processes-based-on-poisson-processes.pdf) - J. Snoek et al. **Practical Bayesian Optimization of Machine Learning Algorithms**, NeurIPS, 2012 [[pdf]](https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf) - M. Hoffman et al. **Stochastic Variational Inference**, JMLR, 2013 [[pdf]](https://arxiv.org/pdf/1206.7051.pdf) - J. Chang et al. **Parallel Sampling of HDPs using Sub-Cluster Splits**, NeurIPS, 2014 [[pdf]](https://papers.nips.cc/paper/5235-parallel-sampling-of-hdps-using-sub-cluster-splits.pdf) - M. Hoffman, **The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo**, JMLR, 2014 [[pdf]](https://arxiv.org/pdf/1111.4246.pdf) - T. Chen et al. **XGBoost: A Scalable Tree Boosting System**, KDD, 2016 [[pdf]](https://arxiv.org/pdf/1603.02754.pdf) - A. Kucukelbir et al. **Automatic Differentiation Variational Inference**, JMLR, 2017 [[pdf]](https://arxiv.org/pdf/1603.00788.pdf) - D. Tran et al. **Deep Probabilistic Programming**, ICLR, 2017 [[pdf]](https://arxiv.org/pdf/1701.03757.pdf) - Y. Koren et al. **Matrix Factorization Techniques for Recommender Systems**, IEEE Computer, 2009 [[pdf]](https://ieeexplore.ieee.org/document/5197422) - G. Ke et al. **LightGBM: A Highly Efficient Gradient Boosting Decision Tree**, NeurIPS, 2017 [[pdf]](https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf) - D. Rezende et al. **Variational Inference with Normalizing Flows**, ICML, 2015 [[pdf]](https://arxiv.org/pdf/1505.05770.pdf) - J. Ho et al. **Denoising Diffusion Probabilistic Models**, NeurIPS, 2020 [[pdf]](https://arxiv.org/pdf/2006.11239.pdf) ### Deep Learning - A. Krizhevsky et al., **ImageNet Classification with Deep Convolutional Neural Networks**, NeurIPS, 2012 [[pdf]](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - J. Bergstra et al., **Random Search for Hyper-Parameter Optimization**, JMLR, 2012 [[pdf]](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf) - M. Lin et al., **Network in Network**, arXiv, 2013 [[pdf]](https://arxiv.org/pdf/1312.4400.pdf) - D. Kingma et al., **Auto-Encoding Variational Bayes**, ICLR, 2014 [[pdf]](https://arxiv.org/pdf/1312.6114.pdf) - I. Goodfellow et al., **Generative Adversarial Nets**, NeurIPS, 2014 [[pdf]](https://arxiv.org/pdf/1406.2661v1.pdf) - N. Srivastava et al., **Dropout: A Simple Way to Prevent Neural Networks from Overfitting**, JMLR, 2014 [[pdf]](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) - K. Simonyan et al., **Very Deep Convolutional Networks for Large-Scale Image Recognition**, ICLR, 2015 [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) - C. Szegedy et al., **Going Deeper with Convolutions**, CVPR, 2015 [[pdf]](https://arxiv.org/pdf/1409.4842v1.pdf) - K. He et al., **Deep Residual Learning for Image Recognition**, arXiv, 2015 [[pdf]](https://arxiv.org/pdf/1512.03385.pdf) - D. Kingma et al., **Adam: A Method for Stochastic Optimization**, ICLR, 2015 [[pdf]](https://arxiv.org/pdf/1412.6980.pdf) - S. Ioffe et al., **Batch Normalization**, ICML, 2015 [[pdf]](https://arxiv.org/pdf/1502.03167.pdf) - F. Iandola et al., **SqueezeNet**, ICLR, 2017 [[pdf]](https://arxiv.org/pdf/1602.07360.pdf) - T. Kipf et al., **Semi-Supervised Classification with Graph Convolutional Networks**, ICLR, 2017 [[pdf]](https://arxiv.org/pdf/1609.02907.pdf) ### Computer Vision - M. Bertalmio et al., **Image Inpainting**, SIGGRAPH, 2000 [[pdf]](http://www.tecn.upf.es/~mbertalmio/bertalmi.pdf) - A. Karpathy et al., **Large-scale Video Classification with Convolutional Neural Networks**, CVPR, 2014 [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf) - A. Davis et al., **The Visual Microphone: Passive Recovery of Sound from Video**, SIGGRAPH, 2014 [[pdf]](https://people.csail.mit.edu/mrub/papers/VisualMic_SIGGRAPH2014.pdf) - O. Vinyals et al., **Show and Tell: A Neural Image Caption Generator**, CVPR, 2015 [[pdf]](https://arxiv.org/pdf/1411.4555.pdf) - L. Gatys et al., **A Neural Algorithm of Artistic Style**, arXiv, 2015 [[pdf]](https://arxiv.org/pdf/1508.06576.pdf) - A. Agrawal et al., **VQA: Visual Question Answering**, ICCV, 2015 [[pdf]](https://arxiv.org/pdf/1505.00468.pdf) - R. Girshick et al., **Fast R-CNN**, ICCV, 2015 [[pdf]](https://arxiv.org/pdf/1504.08083.pdf) - J. Long et al., **Fully Convolutional Networks for Semantic Segmentation**, CVPR, 2015 [[pdf]](https://arxiv.org/pdf/1411.4038.pdf) - J. Redmon et al., **You Only Look Once: Unified, Real-Time Object Detection**, CVPR, 2016 [[pdf]](https://arxiv.org/pdf/1506.02640.pdf) - C. Dong et al., **Image Super-Resolution Using Deep Convolutional Networks**, TPAMI, 2016 [[pdf]](https://arxiv.org/pdf/1501.00092v3.pdf) ### Natural Language Processing - D. Blei et al., **Latent Dirichlet Allocation**, JMLR, 2003 [[pdf]](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) - R. Mihalcea et al., **TextRank: Bringing Order into Texts**, EMNLP, 2004 [[pdf]](https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) - T. Mikolov et al., **Efficient Estimation of Word Representations in Vector Space**, arXiv, 2013 [[pdf]](https://arxiv.org/pdf/1301.3781.pdf) - J. Pennington et al., **GloVe: Global Vectors for Word Representation**, EMNLP, 2014 [[pdf]](https://nlp.stanford.edu/pubs/glove.pdf) - Q. Le et al., **Distributed Representations of Sentences and Documents**, ICML, 2014 [[pdf]](https://arxiv.org/pdf/1405.4053v2.pdf) - I. Sutskever et al., **Sequence to Sequence Learning with Neural Networks**, NeurIPS, 2014 [[pdf]](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf) - Y. Kim et al., **Convolutional Neural Networks for Sentence Classification**, EMNLP, 2014 [[pdf]](https://arxiv.org/pdf/1408.5882.pdf) - D. Bahdanau et al., **Neural Machine Translation by Jointly Learning to Align and Translate**, ICLR, 2015 [[pdf]](https://arxiv.org/pdf/1409.0473.pdf) - A. Vaswani et al., **Attention Is All You Need**, NeurIPS, 2017 [[pdf]](http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf) - J. Devlin et al., **BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**, arXiv, 2018 [[pdf]](https://arxiv.org/pdf/1810.04805.pdf) ### Speech Recognition - E. Fox et al., **A Sticky HDP-HMM with Application to Speaker Diarization**, Annals of Applied Statistics, 2011 [[pdf]](https://arxiv.org/pdf/0905.2592.pdf) - C. Lee et al., **A Nonparametric Bayesian Approach to Acoustic Model Discovery**, ACL, 2012 [[pdf]](https://groups.csail.mit.edu/sls/publications/2012/Lee_ACL_2012.pdf) - N. Jaitly et al., **Application of Pretrained DNNs to Large Vocabulary Speech Recognition**, Interspeech, 2012 [[pdf]](http://www.cs.toronto.edu/~ndjaitly/jaitly-interspeech12.pdf) - A. Graves et al., **Speech Recognition with Deep Recurrent Neural Networks**, ICASSP, 2013 [[pdf]](https://arxiv.org/pdf/1303.5778.pdf) - D. Bahdanau et al., **End-to-End Attention-based Large Vocabulary Speech Recognition**, ICASSP, 2016 [[pdf]](https://arxiv.org/pdf/1508.04395.pdf) ### Reinforcement Learning - V. Mnih et al., **Playing Atari with Deep Reinforcement Learning**, NeurIPS, 2013 [[pdf]](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) - D. Silver et al., **Mastering the game of Go without human knowledge**, Nature, 2017 [[pdf]](https://deepmind.com/documents/119/agz_unformatted_nature.pdf) - C. Finn et al., **Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**, ICML, 2017 [[pdf]](https://arxiv.org/pdf/1703.03400.pdf) ### ML Systems Design - Y. Jing et al., **Visual Search at Pinterest**, KDD, 2015 [[pdf]](https://arxiv.org/pdf/1505.07647.pdf) - P. Nigam et al., **Semantic Product Search**, KDD, 2019 [[pdf]](https://arxiv.org/pdf/1907.00937.pdf) - P. Covington et al., **Deep Neural Networks for YouTube Recommendations**, RecSys, 2016 [[pdf]](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf) - M. Chen et al., **Gmail Smart Compose: Real-Time Assisted Writing**, KDD, 2019 [[pdf]](https://arxiv.org/pdf/1906.00080) ### Machine Learning Engineering - W. Kwon et al., **Efficient Memory Management for Large Language Model Serving with Paged Attention**, SOSP, 2023 [[pdf]](https://arxiv.org/pdf/2309.06180) - L. Zheng et al., **SGLang: Efficient Execution of Structured Language Model Programs**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2312.07104) - T. Dao et al., **FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness**, arXiv, 2022 [[pdf]](https://arxiv.org/pdf/2205.14135) - A. Agrawal et al., **Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2403.02310) - L. Chen et al., **Punica: Multi-Tenant LoRA Serving**, arXiv, 2023 [[pdf]](https://arxiv.org/pdf/2310.18547) ### Generative AI - L. Ouyang et al., **Training Language Models to Follow Instructions with Human Feedback**, arXiv, 2022 [[pdf]](https://arxiv.org/pdf/2203.02155.pdf) - R. Rafailov et al., **Direct Preference Optimization: Your Language Model is Secretly a Reward Model**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2305.18290) - T. Wu et al., **Thinking LLMs: General Instruction Following with Thought Generation**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2410.10630) - J. Betker et al., **Improving Image Generation with Better Captions**, arXiv, 2023 [[pdf]](https://cdn.openai.com/papers/dall-e-3.pdf) - A. Dubey et al., **The Llama 3 Herd of Models**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2407.21783) - E. Hu et al., **LoRA: Low-Rank Adaptation of Large Language Models**, ICLR, 2022 [[pdf]](https://arxiv.org/pdf/2106.09685.pdf) - S. Yao et al., **ReAct: Synergizing Reasoning and Acting in Language Models**, ICLR, 2023 [[pdf]](https://arxiv.org/pdf/2210.03629.pdf) - T. Zhang et al., **RAFT: Apating Language Model to Domain Specific RAG**, arXiv, 2024 [[pdf]](https://arxiv.org/pdf/2403.10131) - G. Hinton et al., **Distilling the Knowledge in a Neural Network**, NeurIPS, 2014 [[pdf]](https://arxiv.org/pdf/1503.02531.pdf) - J. Kaplan et al., **Scaling Laws for Neural Language Models**, arXiv, 2020 [[pdf]](https://arxiv.org/pdf/2001.08361.pdf)
