DGMs 4 NLP. Deep Generative Models for Natural Language Processing. A Roadmap.
Yao Fu, University of Edinburgh, yao.fu@ed.ac.uk
**Update**: How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources
**Update**: A Closer Look at Language Model Emergent Abilities
**Update**: Large Languge Models
**Update**: Long-range Dependency; Why S4 is Good at Long Sequence: Remembering a Sequence with Online Function Approximation
**TODO 1**: Calibration; Prompting; Long-range transformers; State-space Models
**TODO 2**: Matrix Factorization and Word embedding; Kernels; Gaussian Process
**TODO 3**: Relationship between inference and RL;
(written in early 2019, originated from the DGM seminar at Columbia)
Why do we want deep generative models? Because we want to learn basic factors that generate language. Human language contains rich latent factors, the continuous ones might be emotion, intention, and others, the discrete/ structural factors might be POS/ NER tags or syntax trees. Many of them are latent as in most cases, we just observe the sentence. They are also generative: human should produce language based on the overall idea, the current emotion, the syntax, and all other things we can or cannot name.
How to model the generative process of language in a statistically principled way? Can we have a flexible framework that allows us to incorporate explicit supervision signals when we have labels, or add distant supervision or logical/ statistical constraints when we do not have labels but have other prior knowledge, or simply infer whatever makes the most sense when we have no labels or a priori? Is it possible that we exploit the modeling power of advanced neural architectures while still being mathematical and probabilistic? DGMs allow us to achieve these goals.
Let us begin the journey.
- 2013: VAE
- 2014: GAN; Sequence to sequence; Attention Mechanism
- 2015: Normalizing Flow; Difussion Models
- 2016: Gumbel-softmax; Google's Neural Machine Translation System (GNMT)
- 2017: Transformers; ELMo
- 2018: BERT
- 2019: Probing and Bertology; GPT2
- 2020: GPT3; Contrastive Learning; Compositional Generalization; Diffusion Models
- 2021: Prompting; Score-based Generative Models;
- 2022: State-spece Models
- Introduction
- Table of Content
- Resources
- NLP Side
- ML Side
- Advanced Topics
Citation:
@article{yao2019DGM4NLP,
title = "Deep Generative Models for Natual Language Processing",
author = "Yao Fu",
year = "2019",
url = "https://github.com/FranxYao/Deep-Generative-Models-for-Natural-Language-Processing"
}
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How to write Variational Inference and Generative Models for NLP: a recipe. This is strongly suggested for beginners writing papers about VAEs for NLP.
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A Tutorial on Deep Latent Variable Models of Natural Language (link), EMNLP 18
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Latent Structure Models for NLP. ACL 2019 tutorial link
- André Martinns, Tsvetomila Mihaylova, Nikita Nangia, Vlad Niculae.
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Columbia STAT 8201 - Deep Generative Models, by John Cunningham
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Stanford CS 236 - Deep Generative Models, by Stefano Ermon
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U Toronto CS 2541 - Differentiable Inference and Generative Models, CS 2547 Learning Discrete Latent Structures, CSC 2547 Fall 2019: Learning to Search. By David Duvenaud
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U Toronto STA 4273 Winter 2021 - Minimizing Expectations. By Chris Maddison
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Berkeley CS294-158 - Deep Unsupervised Learning. By Pieter Abbeel
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Columbia STCS 8101 - Representation Learning: A Probabilistic Perspective. By David Blei
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Stanford CS324 - Large Language Models. By Percy Liang, Tatsunori Hashimoto and Christopher Re
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U Toronto CSC2541 - Neural Net Training Dynamics. By Roger Grosse.
The fundation of the DGMs is built upon probabilistic graphical models. So we take a look at the following resources
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Blei's Foundation of Graphical Models course, STAT 6701 at Columbia (link)
- Foundation of probabilistic modeling, graphical models, and approximate inference.
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Xing's Probabilistic Graphical Models, 10-708 at CMU (link)
- A really heavy course with extensive materials.
- 5 modules in total: exact inference, approximate inference, DGMs, reinforcement learning, and non-parameterics.
- All the lecture notes, vedio recordings, and homeworks are open-sourced.
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Collins' Natural Language Processing, COMS 4995 at Columbia (link)
- Many inference methods for structured models are introduced. Also take a look at related notes from Collins' homepage
- Also checkout bilibili
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Pattern Recognition and Machine Learning. Christopher M. Bishop. 2006
- Probabily the most classical textbook
- The core part, according to my own understanding, of this book, should be section 8 - 13, especially section 10 since this is the section that introduces variational inference.
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Machine Learning: A Probabilistic Perspective. Kevin P. Murphy. 2012
- Compared with the PRML Bishop book, this book may be used as a super-detailed handbook for various graphical models and inference methods.
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Graphical Models, Exponential Families, and Variational Inference. 2008
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Linguistic Structure Prediction. 2011
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The Syntactic Process. 2000
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Generating Sentences from a Continuous Space, CoNLL 15
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Neural variational inference for text processing, ICML 16
- Yishu Miao, Lei Yu, Phil Blunsom, Deepmind
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Learning Neural Templates for Text Generation. EMNLP 2018
- Sam Wiseman, Stuart M. Shieber, Alexander Rush. Havard
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Residual Energy Based Models for Text Generation. ICLR 20
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Paraphrase Generation with Latent Bag of Words. NeurIPS 2019.
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Fairseq Decoding Library. [github]
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Controllabel Neural Text Generation [Lil'Log]
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Best-First Beam Search. TACL 2020
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The Curious Case of Neural Text Degeneration. ICLR 2020
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Comparison of Diverse Decoding Methods from Conditional Language Models. ACL 2019
- Daphne Ippolito, Reno Kriz, Maria Kustikova, Joa ̃o Sedoc, Chris Callison-Burch
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Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. ICML 19
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Conditional Poisson Stochastic Beam Search. EMNLP 2021
- Clara Meister, Afra Amini, Tim Vieira, Ryan Cotterell
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Massive-scale Decoding for Text Generation using Lattices. 2021
- Jiacheng Xu and Greg Durrett
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Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search. ACL 2017
- Chris Hokamp, Qun Liu
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Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation. NAACL 2018
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Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting. NAACL 2019
- J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
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Towards Decoding as Continuous Optimisation in Neural Machine Translation. EMNLP 2017
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Gradient-guided Unsupervised Lexically Constrained Text Generation. EMNLP 2020
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Controlled Text Generation as Continuous Optimization with Multiple Constraints. 2021
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NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints. NAACL 2021
- Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
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NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics. 2021
- Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi
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COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics. 2022
- Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi
Note: I have not fully gone through this chapter, please give me suggestions!
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Non-Autoregressive Neural Machine Translation. ICLR 2018
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Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade.
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Fast Decoding in Sequence Models Using Discrete Latent Variables. ICML 2021
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Cascaded Text Generation with Markov Transformers. Arxiv 20
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Glancing Transformer for Non-Autoregressive Neural Machine Translation. ACL 2021
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- This one is now deployed inside Bytedance
TODO: more about it
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Prompt Papers, ThuNLP (link)
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CTRL: A Conditional Transformer Language Model for Controllable Generation. Arxiv 2019
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Structured Attention Networks. ICLR 2017
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Differentiable Dynamic Programming for Structured Prediction and Attention. ICML 2018
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Recurrent Neural Network Grammars. NAACL 16
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Unsupervised Recurrent Neural Network Grammars, NAACL 19
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Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder, ICLR 19
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The Syntactic Process. 2020
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Linguistically-Informed Self-Attention for Semantic Role Labeling. EMNLP 2018 Best paper award
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Compositional Generalization in NLP. Paper list
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Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. ICML 2019
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Improving Text-to-SQL Evaluation Methodology. ACL 2018
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Stochastic Variational Inference
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Variational Bayesian Inference with Stochastic Search. ICML 12
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Auto-Encoding Variational Bayes, ICLR 14
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR 2017
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Importance Weighted Autoencoders. ICLR 2015
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML 14
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- Reparameterization w. deep gaussian models.
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Semi-amortized variational autoencoders, ICML 18
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Adversarially Regularized Autoencoders, ICML 18
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More on reparameterization: to reparameterize gaussian mixture, permutation matrix, and rejection samplers(Gamma and Dirichlet).
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Stochastic Backpropagation through Mixture Density Distributions, Arxiv 16
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Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS 2017
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Implicit Reparameterization Gradients. NeurIPS 2018.
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Categorical Reparameterization with Gumbel-Softmax. ICLR 2017
- Eric Jang, Shixiang Gu, Ben Poole
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR 2017
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Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. 2020
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Reparameterizable Subset Sampling via Continuous Relaxations. IJCAI 2019
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Generative Adversarial Networks, NIPS 14
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Towards principled methods for training generative adversarial networks, ICLR 2017
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Wasserstein GAN
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS 2016
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Adversarially Learned Inference. ICLR 2017
- Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, Aaron Courville
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Flow Based Deep Generative Models, from Lil's log
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Variational Inference with Normalizing Flows, ICML 15
- Danilo Jimenez Rezende, Shakir Mohamed
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Learning About Language with Normalizing Flows
- Graham Neubig, CMU, slides
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Improved Variational Inference with Inverse Autoregressive Flow
- Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
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Density estimation using Real NVP. ICLR 17
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Unsupervised Learning of Syntactic Structure with Invertible Neural Projections. EMNLP 2018
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Latent Normalizing Flows for Discrete Sequences. ICML 2019.
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Discrete Flows: Invertible Generative Models of Discrete Data. 2019
- Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
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FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow. EMNLP 2019
- Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy
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Variational Neural Machine Translation with Normalizing Flows. ACL 2020
- Hendra Setiawan, Matthias Sperber, Udhay Nallasamy, Matthias Paulik. Apple
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On the Sentence Embeddings from Pre-trained Language Models. EMNLP 2020
- Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, Lei Li
FY: Need to see how score-based generative models and diffusion models can be used for discrete sequences
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Generative Modeling by Estimating Gradients of the Data Distribution. Blog 2021
- Yang Song
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Score Based Generative Modeling Papers
- researchers at the University of Oxford
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Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 2019
- Yang Song, Stefano Ermon
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What are Diffusion Models? 2021
- Lilian Weng
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- Heejoon Koo
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics. 2015
- Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli
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Denoising Diffusion Probabilistic Models. NeurIPS 2020
- Jonathan Ho, Ajay Jain, Pieter Abbeel
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Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS 2021
- Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
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Structured Denoising Diffusion Models in Discrete State-Spaces. NeurIPS 2021
- Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg
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Autoregressive Diffusion Models. ICLR 2022
- Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans
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Diffusion-LM Improves Controllable Text Generation. 2022
- Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. 2022
- Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi
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Ordered Neurons: Integrating Tree Structured into Recurrent Neural Networks
- Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville. Mila, MSR
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RNNs can generate bounded hierarchical languages with optimal memory
- John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, Christopher D. Manning
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Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned. ACL 2019
- Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, Ivan Titov
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Theoretical Limitations of Self-Attention in Neural Sequence Models. TACL 2019
- Michael Hahn
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Rethinking Attention with Performers. 2020
- Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
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THUNLP: Pre-trained Languge Model paper list (link)
- Xiaozhi Wang and Zhengyan Zhang, Tsinghua University
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Tomohide Shibata's BERT-related Papers
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- Long Range Arena: A Benchmark for Efficient Transformers
- Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler
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HiPPO: Recurrent Memory with Optimal Polynomial Projections. NeurIPS 2020
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Combining Recurrent, Convolutional, and Continuous-time Models with the Linear State Space Layer. NeurIPS 2021
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Efficiently Modeling Long Sequences with Structured State Spaces. ICLR 2022
- Albert Gu, Karan Goel, and Christopher Ré
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Why S4 is Good at Long Sequence: Remembering a Sequence with Online Function Approximation. 2022
- Yao Fu
- Serving OPT-175B using Alpa (350 GB GPU memory in total) link
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GPT3 (175B). Language Models are Few-Shot Learners. May 2020
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Megatron-Turing NLG (530B). Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model. Jan 2022
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LaMDA (137B). LaMDA: Language Models for Dialog Applications. Jan 2022
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Gopher (280B). Scaling Language Models: Methods, Analysis & Insights from Training Gopher. Dec 2021
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Chinchilla (70B). Training Compute-Optimal Large Language Models. Mar 2022
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PaLM (540B). PaLM: Scaling Language Modeling with Pathways. Apr 2022
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OPT (175B). OPT: Open Pre-trained Transformer Language Models. May 2022
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BLOOM (176B): BigScience Large Open-science Open-access Multilingual Language Model. May 2022
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BlenderBot 3 (175B): a deployed conversational agent that continually learns to responsibly engage. Aug 2022
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Scaling Laws for Neural Language Models. 2020
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Emergent Abilities of Large Language Models. 2022
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Minimizing Expectations. Chris Maddison
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Monte Carlo Gradient Estimation in Machine Learning
- Schakir Mohamed, Mihaela Rosca, Michael Figurnov, Andriy Mnih. DeepMind
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Variational Inference for Monte Carlo Objectives. ICML 16
- Andriy Mnih, Danilo J. Rezende. DeepMind
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REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 17
- George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein. Google Brain, DeepMind, Oxford
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Backpropagation Through the Void: Optimizing Control Variates for Black-box Gradient Estimation. ICLR 18
- Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud. U Toronto and Vector Institute
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Backpropagating through Structured Argmax using a SPIGOT. ACL 2018 Best Paper Honorable Mention.
- Hao Peng, Sam Thomson, and Noah A. Smith
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Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning. EMNLP 2020
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Learning with Differentiable Perturbed Optimizers. NeurIPS 2020
- Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach
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Gradient Estimation with Stochastic Softmax Tricks. NeurIPS 2020
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Differentiable Dynamic Programming for Structured Prediction and Attention. ICML 18
- Arthur Mensch, Mathieu Blondel. Inria Parietal and NTT Communication Science Laboratories
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Stochastic Optimization of Sorting Networks via Continuous Relaxations
- Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
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Differentiable Ranks and Sorting using Optimal Transport
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Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS 2018
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A Regularized Framework for Sparse and Structured Neural Attention. NeurIPS 2017
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SparseMAP: Differentiable Sparse Structured Inference. ICML 2018
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Nested Named Entity Recognition with Partially-Observed TreeCRFs. AAAI 2021
- Yao Fu, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang
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Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019.
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Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. NeurIPS 2020
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Posterior Control of Blackbox Generation. 2019
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Dependency Grammar Induction with a Neural Variational Transition-based Parser. AAAI 2019
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(In Chinese) 微分几何与拓扑学简明教程
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Only Bayes Should Learn a Manifold (On the Estimation of Differential Geometric Structure from Data). Arxiv 2018
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The Riemannian Geometry of Deep Generative Models. CVPRW 2018
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The Geometry of Deep Generative Image Models and Its Applications. ICLR 2021
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Metrics for Deep Generative Models. AISTATS 2017
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First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces. 2022
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Telescoping Density-Ratio Estimation. NeurIPS 2020
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Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML 2021
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Randomized Automatic Differentiation. ICLR 2021
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Scaling Structured Inference with Randomization. 2021
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Learning Deep Representations By Mutual Information Estimation And Maximization. ICLR 2019
- R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio
- A detailed comparison between different MI estimators, section 3.2.
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MINE: Mutual Information Neural Estimation
- R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio
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Deep Variational Information Bottleneck. ICLR 2017
- Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy. Google Research
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Identifying Bayesian Mixture Models
- Michael Betancourt
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Disentangling Disentanglement in Variational Autoencoders. ICML 2019
- Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019
- Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
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Emergence of Invariance and Disentanglement in Deep Representations
- Alessandro Achillo and Stefano Soatto. UCLA. JMLR 2018
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Invariant Risk Minimization
- Martin Arjovsky, Leon Bottou, Ishaan Gulrajani, David Lopez-Paz. 2019.
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Fixing a Broken ELBO. ICML 2018.
- Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
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Tighter Variational Bounds are Not Necessarily Better. ICML 2018
- Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
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The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS 2019
- Gabriel Loaiza-Ganem and John P. Cunningham. Columbia.
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Do Deep Generative Models Know What They Don't Know? ICLR 2019
- Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan
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Effective Estimation of Deep Generative Language Models. ACL 2020
- Tom Pelsmaeker and Wilker Aziz. University of Edinburgh and University of Amsterdam
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How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
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A statistical theory of cold posteriors in deep neural networks. ICLR 2021
- Laurence Aitchison
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Limitations of Autoregressive Models and Their Alternatives. NAACL 2021
- Chu-Cheng Lin, Aaron Jaech, Xin Li, Matthew R. Gormley, Jason Eisner