paper-notes

2018-07

• Difficulty Controllable Question Generation for Reading Comprehension [notes] [link]
• Universal Transformers [notes] [link]
• Layer Normalization [Notes] [link]
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [Notes] [link]
• Bilateral Multi-Perspective Matching for Natural Language Sentences [Link] [Notes]
• Deep Contextualized Word Representations [notes]
• A Model To Learn Them All [notes]
• Neural Architecture Search with Reinforcement Learning. [notes]

2018-08

• Asynchronous Methods for Deep Reinforcement Learning [notes]
• Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting [notes]
• Improving Abstraction in Text Summarization [notes]

2019-02

• Learning Unsupervised Learning Rules [notes]

2019-06

• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context [notes]
• XLNet: Generalized Autoregressive Pretraining for Language Understanding [notes]
• COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge [notes]
• AUTOSEM: Automatic Task Selection and Mixing in Multi-Task Learning [notes]

2020-05

• Experience Grounds Language [link], [Notes]
• Proximal Policy Optimization Algorithms [link], [Notes]
• Fine-Tuning Language Models from Human Preferences [link], [Notes]
• Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers [Notes]
• Distributional Reinforcement Learning for Energy-Based Sequential Models [Notes]
• Calibration Of Pretrained Transformers [link], [Notes]
• REALM: Retrieval-Augmented Language Model Pre-Training [link], [Notes]
• Byte pair encoding is suboptimal for Language Model Pretraining [link], [Notes]
• Energy-based Models for Text [link], [Notes]

2020-06

• Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling [link], [Notes]

2020-07

• Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods [link], [Notes]

2021-02

• Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions [link], [Notes]

2021-07

• REPRESENTATION LEARNING VIA INVARIANT CAUSAL MECHANISMS [link], [Notes]