All Research Areas
Research Areas
Year Published

534 Results

December 4, 2017

On the Optimization Landscape of Tensor Decompositions

Neural Information Processing Systems (NIPS)

In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised learning, especially in learning latent variable models. In practice, it can be efficiently solved by gradient ascent on a non-convex objective.

By: Rong Ge, Tengyu Ma
December 4, 2017

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

Neural Information Processing Systems (NIPS)

We present a novel training framework for neural sequence models, particularly for grounded dialog generation.

By: Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra
December 4, 2017

Fader Networks: Manipulating Images by Sliding Attributes

Neural Information Processing Systems (NIPS)

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.

By: Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
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 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
November 27, 2017

Casual 3D Photography

SIGGRAPH ASIA

We present an algorithm that enables casual 3D photography. Given a set of input photos captured with a hand-held cell phone or DSLR camera, our algorithm reconstructs a 3D photo, a central panoramic, textured, normal mapped, multi-layered geometric mesh representation. Our geometric representation also allows interacting with the scene using 3D geometry-aware effects, such as adding new objects to the scene and artistic lighting effects.

By: Peter Hedman, Suhib Alsisan, Richard Szeliski, Johannes Kopf