Papers I Read Notes and Summaries

When to use parametric models in reinforcement learning?

Introduction

  • The paper compares replay-based approaches with model-based approaches in Reinforcement Learning...


Network Randomization - A Simple Technique for Generalization in Deep Reinforcement Learning

Introduction

  • The paper proposed a Technique for improving the generalization ability of...


On the Difficulty of Warm-Starting Neural Network Training

Introduction

  • The paper considers learning scenarios where the training data is available...


Supervised Contrastive Learning

Introduction

  • The paper builds on the prior work on self-supervised contrastive learning...


CURL - Contrastive Unsupervised Representations for Reinforcement Learning

Introduction

  • The paper proposes a contrastive learning approach, called CURL, for performing...


Competitive Training of Mixtures of Independent Deep Generative Models

Introduction

  • The paper proposes a Competitive training mechanism to train a mixture...


What Does Classifying More Than 10,000 Image Categories Tell Us?

  • The paper is among the first to study image classification at a large...


mixup - Beyond Empirical Risk Minimization

Introduction

  • The paper proposes a simple and dataset-agnostic data augmentation mechanism called...


ELECTRA - Pre-training Text Encoders as Discriminators Rather Than Generators

Introduction

  • Masked Language Modeling (MLM) is a common technique for pre-training language-based...


Gradient based sample selection for online continual learning

Introduction

  • Use of replay buffer (and rehearsal) is a common technique for...