Links to papers I've read and gleaned insights

To Read:

NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING

+

Learning What and Where to Draw

• Notes:

Recurrent Highway Networks

• Notes:

Bootstrapping Dialog Systems with Word Embeddings

• Notes:

Language Understanding for Text-based Games Using Deep Reinforcement Learning

• Notes:

Learning to Compose Neural Networks for Question Answering

• Notes:

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

• Notes:

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

• Notes:

DeepMath - Deep Sequence Models for Premise Selection

• Notes:

NLP Oriented

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

• Date: Jul 3, 2016
• Notes:
  • Initialized their word embeddings with word2vec trained on google news dataset
  • Used

End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning

• Date: Jul 2, 2016
• Notes:
  • 3 part system
    • lstm - 32 cells, 1 layer deep
    • parser
    • state based software api thing

Matching Networks for One Shot Learning

• Date: 6/15/16
• Notes: +

Learning End-to-End Goal-Oriented Dialog

• Date: 6/5/16
• Notes:
  • storing conversational data (sec 4.)

Globally Normalized Transition-Based Neural Networks

• Date: 6/10/16
• notes:
  • A.k.a. syntaxnet
  • Feedforward network. Basis of improvement on normalization versus LSTM/residual networks.
  • Original google blog post here.

• notes:
  • a.k.a. syntaxnet.
  • Feedforward network. Basis of improvement on normalization versus LSTM/residual networks.
  • Original google blog post here.

Sequence to Sequence Learning with Neural Networks

Notes:

CV/CNN Oriented

RL Oriented

Deep Reinforcement Learning: Pong from Pixels

• Date: 6/15/16
• notes: +

Templates:

• Date:
• Notes: