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198 Results

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

Learning through Dialogue Interactions by Asking Questions

International Conference on Learning Representations (ICLR) 2017

In this work, we explore a dialogue agents ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction, by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher.

By: Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
April 24, 2017

Dialogue Learning with Human-in-the-Loop

International Conference on Learning Representations (ICLR) 2017

In this paper we explore interacting with a dialogue partner in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses.

By: Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
April 24, 2017

Training Agent for First-Person Shooter Game With Actor-Critic Curriculum Learning

International Conference on Learning Representations (ICLR) 2017

In this paper, we propose a new framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom.

By: Yuxin Wu, Yuandong Tian
April 24, 2017

Unsupervised Cross-Domain Image Generation

International Conference on Learning Representations (ICLR) 2017

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given representation function f, which accepts inputs in either domains, would remain unchanged.

By: Yaniv Taigman, Adam Polyak, Lior Wolf
April 24, 2017

An Analytical Formula of Population Gradient for Two-Layered ReLU network and its Applications in Convergence and Critical Point Analysis

International Conference on Learning Representations (ICLR) 2017

In this paper, we explore theoretical properties of training a two-layered ReLU network g(x; w) = PK j=1 σ(w | j x) with centered d-dimensional spherical Gaussian input x (σ=ReLU). We train our network with gradient descent on w to mimic the output of a teacher network with the same architecture and fixed parameters w∗.

By: Yuandong Tian
April 24, 2017

LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

International Conference on Learning Representations (ICLR)

We present LR-GAN: an adversarial image generation model which takes scene structure and context into account.

By: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
April 24, 2017

Towards Principled Methods for Training Generative Adversarial Networks

International Conference on Learning Representations (ICLR) 2017

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks.

By: Martin Arjovsky, Leon Bottou