Papers I Read Notes and Summaries

Imagination-Augmented Agents for Deep Reinforcement Learning

  • The paper presents I2A (Imagination Augmented Agent) that combines the model-based and model-free approaches leading to data efficiency... Continue reading

Kronecker Recurrent Units


  • Recurrent Neural Networks have two key issues:

Learning Independent Causal Mechanisms


  • The paper presents a very interesting approach for learning independent (inverse) data transformation from a set... Continue reading

Memory-based Parameter Adaptation


  • Standard Deep Learning networks are not suitable for continual learning setting as the change in the... Continue reading

Born Again Neural Networks


  • The paper explores knowledge distillation (KD) from the perspective of transferring knowledge between 2 networks of... Continue reading

Net2Net-Accelerating Learning via Knowledge Transfer


  • The paper presents a simple yet effective approach for transferring knowledge from a trained neural network... Continue reading

Learning to Count Objects in Natural Images for Visual Question Answering


  • Most of the visual question-answering (VQA) models perform poorly on the task of counting objects in an... Continue reading

Neural Message Passing for Quantum Chemistry


  • The paper presents a general message passing architecture called as Message Passing Neural Networks (MPNNs) that... Continue reading

Unsupervised Learning by Predicting Noise


  • Convolutional Neural Networks are extremely good feature extractors in the sense that features extracted for one... Continue reading

The Lottery Ticket Hypothesis - Training Pruned Neural Networks


  • Empirical evidence indicates that at training time, the neural networks need to be of significantly larger... Continue reading