NeurIPS 2016

Domain Separation Networks

NeurIPS 2016 tensorflow/models

However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.

UNSUPERVISED DOMAIN ADAPTATION

Can Active Memory Replace Attention?

NeurIPS 2016 tensorflow/models

Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years.

IMAGE CAPTIONING MACHINE TRANSLATION

Improved Techniques for Training GANs

NeurIPS 2016 tensorflow/models

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.

CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

NeurIPS 2016 tensorflow/models

This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.

IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

NeurIPS 2016 tensorflow/models

We study the problem of synthesizing a number of likely future frames from a single input image.

Unsupervised Learning for Physical Interaction through Video Prediction

NeurIPS 2016 tensorflow/models

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment.

VIDEO PREDICTION

Deep Exploration via Bootstrapped DQN

NeurIPS 2016 tensorflow/models

Efficient exploration in complex environments remains a major challenge for reinforcement learning.

ATARI GAMES EFFICIENT EXPLORATION

R-FCN: Object Detection via Region-based Fully Convolutional Networks

NeurIPS 2016 facebookresearch/detectron

In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.

REAL-TIME OBJECT DETECTION

Learning to learn by gradient descent by gradient descent

NeurIPS 2016 deepmind/learning-to-learn

The move from hand-designed features to learned features in machine learning has been wildly successful.

META-LEARNING

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

NeurIPS 2016 tkipf/gcn

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

NODE CLASSIFICATION