SIPB Deep Learning Group

The schedule of readings for the SIPB/Cambridge AI Deep Learning Group 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
8.21.18	Universal Transformers	tensorflow
8.14.18	Neural Arithmetic Logic Units	gautam1858
8.7.18	Neural Scene Representation and Rendering
7.31.18	Measuring Abstract Reasoning in Neural Networks
6.26.18	Improving Language Understanding by Generative Pre-Training	openai
6.19.18	Associative Compression Networks for Representation Learning
6.12.18	On Characterizing the Capacity of Neural Networks using Algebraic Topology
6.5.18	Causal Effect Inference with Deep Latent-Variable Models	AMLab
5.29.18	ML beyond Curve Fitting
5.22.18	Synthesizing Programs for Images using Reinforced Adversarial Learning
5.15.18	TensorFlow Overview	r1.8
5.8.18	Compositional Attention Networks for Machine Reasoning	stanfordnlp
4.24.18	The Annotated Transformer
4.3.18	How Developers Iterate on Machine Learning Workflows
3.27.18	Faster R-CNN: Towards Real-Time Object,Detection with Region Proposal Networks
3.20.18	Attention Is All You Need	tensor2tensor
3.6.18	Generating Wikipedia by Summarizing Long Sequences	wikisum, per this gist
2.27.18	AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks	StackGAN-v2
2.20.18	Information Dropout	InformationDropout, official implementation
2.13.18	Nested LSTMs	Nested-LSTM
2.6.18	Deep vs. Shallow Networks: An Approximation Theory Perspective
1.30.18	The Case for Learned Index Structures
1.23.18	Visualizing The Loss Landscape Of Neural Nets
1.16.18	Go for a Walk and Arrive at the Answer, RelNet: End-to-End Modeling of Entities & Relations
1.9.18	Intro to Coq
12.12.17	Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks	(ChainsofReasoning)
12.5.17	Stochastic Neural Networks for Hierarchical Reinforcement Learning	snn4hrl
11.28.17	Emergent Complexity via Multi-Agent Competition (blog post)	multiagent-competition
11.14.17	Mastering the game of Go without human knowledge
11.7.17	Meta-Learning with Memory-Augmented Neural Networks	ntm-meta-learning
10.24.17	Poincaré Embeddings for Learning Hierarchical Representations	poincare_embeddings
10.17.17	What does Attention in Neural Machine Translation Pay Attention to?
10.10.17	Zero-Shot Learning Through Cross-Modal Transfer	zslearning
9.26.17	Variational Boosting: Iteratively Refining Posterior Approximations	vboost
9.19.17	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	cbfinn
9.12.17	Neuroscience-inspired AI
9.5.17	Recurrent Dropout Without Memory Loss	rnn_cell_mulint_modern.py
8.29.17	Deep Transfer Learning with Joint Adaptation Networks	jmmd.{cpp,hpp}
8.22.17	Designing Neural Network Architectures using Reinforcement Learning	metaqnn
8.15.17	Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences	plstm
8.8.17	Hyper Networks	otoro blog
8.1.17	Full-Capacity Unitary Recurrent Neural Networks	complex_RNN, urnn
7.25.17	Decoupled Neural Interfaces using Synthetic Gradients & follow-up	dni.pytorch
7.18.17	A simple neural network module for relational reasoning	relation-network
7.11.17	Speaker diarization using deep neural network embeddings
6.20.17	Neural Episodic Control	PFCM
6.13.17	Lie-Access Neural Turing Machines	harvardnlp
6.6.17	Artistic style transfer for videos	artistic video
5.30.17	High-Dimensional Continuous Control Using Generalized Advantage Estimation	modular_rl
5.23.17	Emergence of Grounded Compositional Language in Multi-Agent Populations
5.16.17	Trust Region Policy Optimization	modular_rl
5.9.17	Improved Training of Wasserstein GANs	code
5.4.17	Using Fast Weights to Attend to the Recent Past
4.25.17	Strategic Attentive Writer for Learning Macro-Actions
4.18.17	Massive Exploration of Neural Machine Translation Architectures
4.4.17	End to End Learning for Self-Driving Cars
3.28.17	Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
3.21.17	Image-to-Image Translation with Conditional Adversarial Networks
3.7.17	Neural Programmer Interpreters
2.14.17	Wasserstein GAN
2.7.17	Towards Principled Methods for Training GANs
1.31.17	Mastering the Game of Go with Deep Networks
1.24.17	Understanding Deep Learning Requires Rethinking Generalization
1.17.17	Neural Semantic Encoders
12.21.16	StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
12.14.16	Key-Value Memory Networks for Directly Reading Documents
12.7.16	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

rodrigogonzalez / cambridge-ai Goto Github PK

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SIPB Deep Learning Group

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