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236 Results

December 4, 2017

One-Sided Unsupervised Domain Mapping

Neural Information Processing Systems (NIPS)

In this work, we present a method of learning GAB without learning GBA. This is done by learning a mapping that maintains the distance between a pair of samples.

By: Sagie Benaim, Lior Wolf
December 4, 2017

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Neural Information Processing Systems (NIPS)

In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research.

By: Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick
December 4, 2017

Unbounded Cache Model for Online Language Modeling with Open Vocabulary

Neural Information Processing Systems (NIPS)

In this paper, we propose an extension of continuous cache models, which can scale to larger contexts. In particular, we use a large scale non-parametric memory component that stores all the hidden activations seen in the past.

By: Edouard Grave, Moustapha Cisse, Armand Joulin
December 4, 2017

VAIN: Attentional Multi-agent Predictive Modeling

Neural Information Processing Systems (NIPS)

In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems.

By: Yedid Hoshen
December 4, 2017

Gradient Episodic Memory for Continual Learning

Neural Information Processing Systems (NIPS)

One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks.

By: David Lopez-Paz, Marc'Aurelio Ranzato
December 4, 2017

Poincaré Embeddings for Learning Hierarchical Representations

Neural Information Processing Systems (NIPS)

In this work, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space – or more precisely into an n-dimensional Poincaré ball.

By: Maximilian Nickel, Douwe Kiela
December 1, 2017

Learning Neural Audio Embeddings for Grounding Semantics in Auditory Perception

Journal of Artificial Intelligence Research, Vol. 60

In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics. After having shown the quality of such auditorily grounded representations, we show how they can be applied to tasks where auditory perception is relevant, including two unsupervised categorization experiments, and provide further analysis.

By: Douwe Kiela, Stephen Clark
October 24, 2017

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

AAAI Conference on Human Computation and Crowdsourcing (HCOMP)

In this work, we design a cooperative game – GuessWhich – to measure human-AI team performance in the specific context of the AI being a visual conversational agent.

By: Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
October 22, 2017

Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

International Conference on Computer Vision (ICCV)

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. To address the challenges in this area, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions.

By: Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele
October 22, 2017

Unsupervised Creation of Parameterized Avatars

International Conference on Computer Vision (ICCV)

We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping’s objective is to have the output image as similar as possible to the input image.

By: Lior Wolf, Yaniv Taigman, Adam Polyak