zia-hasan/research

List of Awesome Papers in NLP/Generative Modeling/Others

Papers and Links

NLP [Tasks][Preprocessing][NLP Neural History][Jacon Eisenstein NLP Notes]

• Improving Language Understanding by Generative Pre-Training[code] [blog]
• Language Models are Unsupervised Multitask Learners [code] [blog]
• Multi-Task Deep Neural Networks for Natural Language Understanding
• Language GANs Falling Short
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [tutorial1][tutorial2][tutorial3][code]
• Get To The Point: Summarization with Pointer-Generator Networks
• Semi-supervised Sequence Learning
• Pointer Networks
• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
• Universal Transformers
• Dual Learning for Machine Translation
• A Survey of the Usages of Deep Learning in Natural Language Processing
• Language Generation with Recurrent Generative Adversarial Networks without Pre-training
• Convolutional Sequence to Sequence Learning
• Transformer-XL: Language Modeling with Longer-Term Dependency
• A Primer on Neural Network Models for Natural Language Processing
• Text Understanding from Scratch
• ELMo: Deep contextualized word representations [allennlp]
• Adaptive Input Representations for Neural Language Modeling
• Toward Controlled Generation of Text
• Unsupervised Natural Language Generation with Denoising Autoencoders
• Adversarial Generation of Natural Language
• Deep Reinforcement Learning for Dialogue Generation
• A Unified Architecture for Natural Language Processing [classic]
• Reasoning about Entailment with Neural Attention
• Learning End-to-End Goal-Oriented Dialog
• Neural Approaches to Conversational AI
• Machine Learning for Dialog State Tracking: A Review
• Neural Machine Translation of Rare Words with Subword Units
• Improving Neural Machine Translation Models with Monolingual Data
• On Tree-Based Neural Sentence Modeling
• Word Translation Without Parallel Data
• Language as a latent variable: Discrete generative models for sentence compression
• Deep Learning in Neural Networks: An Overview
• Paraphrase Generation with Deep Reinforcement Learning

GAN

• Spectral Normalization for Generative Adversarial Networks
• MaskGAN: Better Text Generation via Filling in the______
• A Note on the Inception Score
• Self-Attention Generative Adversarial Networks [SAGAN]
• SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [SeqGAN]
• Wasserstein GAN [WGAN]
• The relativistic discriminator: a key element missing from standard GAN [blog1][blog2][code][Relativistic GANs]
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [InfoGAN]
• Conditional Generative Adversarial Nets [CoGAN]
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (CycleGAN)
• Autoencoding beyond pixels using a learned similarity metric [VAEGAN]
• Auto-Encoding Variational Bayes (VAE)
• BEGAN: Boundary Equilibrium Generative Adversarial Networks [BEGAN]
• Unsupervised Image-to-Image Translation Networks (UNIT) [code]
• Multimodal Unsupervised Image-to-Image Translation (MUNIT) [code]
• Coupled Generative Adversarial Networks [code][coGAN]
• Diverse Image-to-Image Translation via Disentangled Representations (MUNIT's Bro)
• Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data [MUNIT's Bro]
• XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings (MUNIT's Bro)
• Toward Multimodal Image-to-Image Translation [code] [BicyleGAN]
• Recycle-GAN: Unsupervised Video Retargeting
• Adversarial Autoencoders
• Wasserstein Auto-Encoders
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
• Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
• Bayesian GAN
• Progressive Growing of GANs for Improved Quality, Stability, and Variation [code]
• Adversarially Learned Inference
• Improved Variational Inference with Inverse Autoregressive Flow
• CyCADA: Cycle-Consistent Adversarial Domain Adaptation
• Disentangling by Factorising
• Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
• Generating a Fusion Image: One’s Identity and Another’s Shape
• Glow: Generative Flow with Invertible 1×1 Convolutions [blog] [code]
• Are GANs Created Equal? A Large-Scale Study
• Adversarially Regularized Autoencoders
• Generalized Denoising Auto-Encoders as Generative Models
• Adversarial examples for generative models
• GAN Q-learning [code]
• AdaGAN: Boosting Generative Models
• Unrolled Generative Adversarial Networks [code]
• Do GANs actually learn the distribution? An empirical study
• StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [code]
• CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms [code]
• Variational Inference: A Review for Statisticians
• Variational Inference with Normalizing Flows
• Generative Adversarial Imitation Learning
• CariGANs: Unpaired Photo-to-Caricature Translation [website]
• A Style-Based Generator Architecture for Generative Adversarial Networks
• CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

GAN Training

• Towards Principled Methods for Training Generative Adversarial Networks
• Improved Training of Wasserstein GANs
• Improved Techniques for Training GANs

Optimization

• Differentiable Learning-to-Normalize via Switchable Normalization
• Layer Normalization
• Instance Normalization: The Missing Ingredient for Fast Stylization
• Group Normalization
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
• On the Convergence of Adam and Beyond
• An overview of gradient descent optimization algorithms

Other Important Ones (RL/Vision etc)

• Taskonomy: Disentangling Task Transfer
• Neural Ordinary Differential Equations
• Squeeze and Excitation Networks
• Pixel Recurrent Neural Networks
• WaveNet: A Generative Model for Raw Audio [Blog]
• Parallel WaveNet: Fast High-Fidelity Speech Synthesis
• Tacotron: Towards End-to-End Speech Synthesis
• Tacotron series
• Deep Voice: Real-time Neural Text-to-Speech
• Deep Voice 2: Multi-Speaker Neural Text-to-Speech
• Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
• Neural Voice Cloning with a Few Samples
• Dynamic Routing Between Capsules
• Deep Learning Techniques for Music Generation – A Survey
• Understanding deep learning requires rethinking generalization
• Neural Turing Machines[Blog1][Blog2]
• Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation
• Opening the black box of deep learning
• Opening the Black Box of Deep Neural Networks via Information
• Everybody Dance Now[Blog]
• Mask R-CNN [blog]

Blog Articles and Links

• Real-time Object Detection with YOLO, YOLOv2 and now YOLOv3
• Object Recognition: [part1][part2][part3]
• Reinforcement Learning:
  • The Multi-Armed Bandit Problem and Its Solutions [Link2]
  • A (Long) Peek into Reinforcement Learning
  • Policy Gradient Algorithms
  • Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym
• Generative Models:
  • Flow-based Deep Generative Models
  • From Autoencoder to Beta-VAE
• Glow: Better Reversible Generative Models
• Reinforcement Learning with Prediction-Based Rewards
• A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs [article]
• Best Papers ACL 2018 / Best Paper Honourable Mentions
• NLP is fun
• Gumbel softmax
• DeepStack, AlphaZero, TRPO
• Generalized Language Models
• The Artificial Intelligence Wiki
• A Knowledge Base for the FB Group Artificial Intelligence and Deep Learning
• SOTA Links
• Papers with Code

Courses

• CS 236: Deep Generative Models [Notes]
• CS228: PGMs
• CS 294-131 Special Topics in Deep Learning (Fall 2016)
• CS 294-131: Special Topics in Deep Learning (Spring 2017)
• CS 294-131: Special Topics in Deep Learning (Fall 2017)
• CS 294-131: Special Topics in Deep Learning (Spring 2018)
• MIT Deep Learning

Books

• Deep Learning Book
• Speech and Language Processing