A curated list of awesome Machine Learning Papers, Repositories. Inspired by awesome-machine-learning.
This lists is based on [Project] All Code Implementations for NIPS 2016 papers
- Using Fast Weights to Attend to the Recent Past
- Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu, Paper
- Learning to learn by gradient descent by gradient descent
- Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas, Paper
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Jifeng Dai, Yi Li, Kaiming He, Jian Sun, Paper
- Fast and Provably Good Seedings for k-Means
- Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause, Paper
- How to Train a GAN
- Soumith Chintala, Emily Denton, Martin Arjovsky, Michael Mathieu
- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
- Daniel Neil, Michael Pfeiffer, Shih-Chii Liu, Paper
- Generative Adversarial Imitation Learning
- Jonathan Ho, Stefano Ermon, Paper
- Adversarial Multiclass Classification: A Risk Minimization Perspective
- Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart, Paper
- Unsupervised Learning for Physical Interaction through Video Prediction
- Chelsea Finn, Ian Goodfellow, Sergey Levine, Paper
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
- Tim Salimans, Diederik P. Kingma, Paper
- Full-Capacity Unitary Recurrent Neural Networks
- Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les Atlas, Paper
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Paper
- Interpretable Distribution Features with Maximum Testing Power
- Wittawat Jitkrittum, Zoltán Szabó, Kacper Chwialkowski, Arthur Gretton, Paper
- Composing graphical models with neural networks for structured representations and fast inference
- Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams, Paper
- Supervised Learning with Tensor Networks
- E. Miles Stoudenmire, David J. Schwab, Paper
- Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation:
- George Papamakarios, Iain Murray, Paper
- Bayesian Optimization for Probabilistic Programs
- Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne Frank Wood, Paper
- PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- Sanghoon Hong, Byungseok Roh, Kye-Hyeon Kim, Yeongjae Cheon, Minje Park, Paper
- Data Programming: Creating Large Training Sets Quickly
- Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré, Paper
- Convolutional Neural Fabrics for Architecture Learning
- Shreyas Saxena, Jakob Verbeek, Paper
- Value Iteration Networks in TensorFlow
- Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel, Paper
- Stochastic Variational Deep Kernel Learning
- Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing, Paper
- Unsupervised Domain Adaptation with Residual Transfer Networks
- Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan, Paper
- Binarized Neural Networks
- Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio, Paper