Papers I Read Notes and Summaries

Learned Optimizers that Scale and Generalize


  • The paper introduces a learned gradient descent optimizer that has low memory and computational overhead and... Continue reading

One-shot Learning with Memory-Augmented Neural Networks


  • The paper demonstrates that Memory Augmented Neural Networks (MANN) are suitable for one-shot learning by introducing... Continue reading

BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop


  • BabyAI is a research platform to investigate and support the feasibility of including humans in the... Continue reading

Poincaré Embeddings for Learning Hierarchical Representations


  • Much of the work in representation leaning uses Euclidean vector spaces to embed datapoints (like words,... Continue reading

When Recurrent Models Don’t Need To Be Recurrent


  • The paper explores “if a well behaved RNN can be replaced by a feed-forward network of... Continue reading

HoME - a Household Multimodal Environment


  • Environment for learning using modalities like vision, audio, semantics, physics and interaction with objects and other... Continue reading

Emergence of Grounded Compositional Language in Multi-Agent Populations


  • The paper provides a multi-agent learning environment and proposes a learning approach that facilitates the emergence... Continue reading

A Semantic Loss Function for Deep Learning with Symbolic Knowledge


  • The paper proposes an approach for using symbolic knowledge in deep learning systems. These constraints are... Continue reading

Hierarchical Graph Representation Learning with Differentiable Pooling


  • Most existing GNN (Graph Neural Network) methods are inherently flat and are unable to process the... Continue reading

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