SIPB Deep Learning Group

The schedule of readings for the SIPB/Cambridge AI Deep Learning Group. The next meeting date and paper is in the schedule in bold. If you have any papers you'd like to discuss, please either make a pull request, or send an email to the group and we'll add it. Papers with implementations available are strongly preferred.

Schedule:

Date	Paper	Implementation
12.7.16	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
12.14.16	Key-Value Memory Networks for Directly Reading Documents
12.21.16	StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
1.17.17	Neural Semantic Encoders
1.24.17	Understanding Deep Learning Requires Rethinking Generalization
1.31.17	Mastering the Game of Go with Deep Networks
2.7.17	Towards Principled Methods for Training GANs
2.14.17	Wasserstein GAN
3.7.17	Neural Programmer Interpreters
3.21.17	Image-to-Image Translation with Conditional Adversarial Networks
3.28.17	Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
4.4.17	End to End Learning for Self-Driving Cars
4.18.17	Massive Exploration of Neural Machine Translation Architectures
4.25.17	Strategic Attentive Writer for Learning Macro-Actions
5.4.17	Using Fast Weights to Attend to the Recent Past
5.9.17	Improved Training of Wasserstein GANs	code
5.16.17	Trust Region Policy Optimization	modular_rl
5.23.17	Emergence of Grounded Compositional Language in Multi-Agent Populations
5.30.17	High-Dimensional Continuous Control Using Generalized Advantage Estimation	modular_rl
6.6.17	Artistic style transfer for videos	artistic video
6.13.17	Lie-Access Neural Turing Machines	harvardnlp
6.20.17	Neural Episodic Control	PFCM
7.11.17	Speaker diarization using deep neural network embeddings
7.18.17	A simple neural network module for relational reasoning	relation-network
7.25.17	Decoupled Neural Interfaces using Synthetic Gradients & follow-up	dni.pytorch

jonathanzlin / cambridge-ai Goto Github PK

cambridge-ai's Introduction

SIPB Deep Learning Group

Suggested Papers:

Schedule:

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