Research Area
Year Published

514 Results

July 21, 2017

Discovering Causal Signals in Images

CVPR 2017

This paper establishes the existence of observable footprints that reveal the “causal dispositions” of the object categories appearing in collections of images.

By: David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, Leon Bottou

July 21, 2017

Relationship Proposal Networks

Conference on Computer Vision and Pattern Recognition 2017

In this paper we address the challenges of image scene object recognition by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions.

By: Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal

July 21, 2017

Link the head to the “beak”: Zero Shot Learning from Noisy Text Description at Part Precision

CVPR 2017

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. F

By: Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal

July 21, 2017

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CVPR 2017

We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires.

By: Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, Larry Zitnick, Ross Girshick

June 8, 2017

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Data @ Scale

In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.

By: Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

May 31, 2017

Modout: Learning Multi-modal Architectures by Stochastic Regularization

IEEE Conference on Automatic Face and Gesture Recognition (FG 2017)

This paper describes Modout, a model selection method based on stochastic regularization, which is particularly useful in the multi-modal setting.

By: Fan Li, Natalia Neverova, Christian Wolf, Graham Taylor

May 21, 2017

CAN: Creative Adversarial Networks

IEEE International Conference on Communications (ICCC)

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution.

By: Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone

May 2, 2017

Better Computer Go Player with Neural Network and Long-Term Prediction

International Conference on Learning Representations (ICLR)

Competing with top human players in the ancient game of Go has been a longterm goal of artificial intelligence. Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not strictly necessary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural Network (DCNN) that predicts the next move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source Go engines such as Pachi [Baudis & Gailly (2012)] if its search budget is limited. We extend this idea in our bot named darkforest, which relies on a DCNN designed for long-term predictions.

By: Yuandong Tian, Yan Zhu

April 24, 2017

Episodic Exploration for Deep Deterministic Policies for StarCraft Micro-Management

International Conference on Learning Representations (ICLR) 2017

We consider scenarios from the real-time strategy game StarCraft as benchmarks for reinforcement learning algorithms.

By: Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala

April 24, 2017

Multi-Agent Cooperation and the Emergence of (Natural) Language

International Conference on Learning Representations (ICLR) 2017

This paper proposes a framework for language learning that relies on multi-agent communication.

By: Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni