NIPS2017 accepted paper is open!

NIPS 2017

I note interesting Research Paper, Workshop on NIPS2017.

e.g. Machine Learning on the Phone and other Consumer Devices(Wow! NIPS is strong theoretical conference but Nowadays Machine Leaning used Industrial Field!)

- Deep Learning: Practice and Trends
- Reinforcement Learning with People
- Powering the next 100 years
- Do Deep Neural Networks Suffer from Crowding?
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
- Deep Subspace Clustering Networks
- Learning Graph Representations with Embedding Propagation
- Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
- Improved Graph Laplacian via Geometric Self-Consistency
- Multitask Spectral Learning of Weighted Automata
- FALKON: An Optimal Large Scale Kernel Method
- Recursive Sampling for the Nystrom Method
- SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
- Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
- Pose Guided Person Image Generation
- Reconstruct & Crush Network
- Real Time Image Saliency for Black Box Classifiers
- Protein Interface Prediction using Graph Convolutional Networks
- A simple model of recognition and recall memory
- Cross-Spectral Factor Analysis
- Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
- Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
- From which world is your graph
- On clustering network-valued data
- K-Medoids For K-Means Seeding
- Clustering Billions of Reads for DNA Data Storage
- Eigen-Distortions of Hierarchical Representations
- Sparse Embedded k-Means Clustering
- A New Theory for Matrix Completion
- Deep Mean-Shift Priors for Image Restoration
- Learning Affinity via Spatial Propagation Networks
- Non-Stationary Spectral Kernels
- Hierarchical Clustering Beyond the Worst-Case
- CTRL-Labs: Non-invasive Neural Interface
- Deep Robotic Learning using Visual Imagination and Meta-Learning
- Sensomind: Democratizing deep learning for the food industry
- Deep Learning for Robotics
- Inverse Reward Design
- Online Learning with a Hint

- Interpretable Machine Learning
- Transparent and interpretable Machine Learning in Safety Critical Environments
- Machine Learning for the Developing World
- ML Systems Workshop @ NIPS 2017
- Machine Learning on the Phone and other Consumer Devices
- NIPS Highlights (MLTrain), Learn How to code a paper with state of the art frameworks
- 2017 NIPS Workshop on Machine Learning for Intelligent Transportation Systems
- Teaching Machines, Robots, and Humans