Research Area
Year Published

1012 Results

December 13, 2019

PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments

Neural Information Processing Systems (NeurIPS)

Given a set of a reference RGBD views of an indoor environment, and a new viewpoint, our goal is to predict the view from that location. Prior work on new-view generation has predominantly focused on significantly constrained scenarios, typically involving artificially rendered views of isolated CAD models. Here we tackle a much more challenging version of the problem. We devise an approach that exploits known geometric properties of the scene (per-frame camera extrinsics and depth) in order to warp reference views into the new ones.

By: David Novotny, Benjamin Graham, Jeremy Reizenstein

December 13, 2019

Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition

Automatic Speech Recognition and Understanding Workshop

The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions.

By: Adrien Dufraux, Emmanuel Vincent, Awni Hannun, Armelle Brun, Matthijs Douze

December 12, 2019

Compositional generalization through meta sequence-to-sequence learning

Neural Information Processing Systems (NeurIPS)

People can learn a new concept and use it compositionally, understanding how to “blicket twice” after learning how to “blicket.” In contrast, powerful sequence-to-sequence (seq2seq) neural networks fail such tests of compositionality, especially when composing new concepts together with existing concepts. In this paper, I show how memory-augmented neural networks can be trained to generalize compositionally through meta seq2seq learning.

By: Brenden Lake

December 11, 2019

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias

Neural Information Processing Systems (NeurIPS)

Despite the phenomenal success of deep neural networks in a broad range of learning tasks, there is a lack of theory to understand the way they work. In particular, Convolutional Neural Networks (CNNs) are known to perform much better than Fully-Connected Networks (FCNs) on spatially structured data: the architectural structure of CNNs benefits from prior knowledge on the features of the data, for instance their translation invariance. The aim of this work is to understand this fact through the lens of dynamics in the loss landscape.

By: Stéphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli

December 11, 2019

Hyper-Graph-Network Decoders for Block Codes

Neural Information Processing Systems (NeurIPS)

Neural decoders were shown to outperform classical message passing techniques for short BCH codes. In this work, we extend these results to much larger families of algebraic block codes, by performing message passing with graph neural networks.

By: Eliya Nachmani, Lior Wolf

December 10, 2019

PHYRE: A New Benchmark for Physical Reasoning

Neural Information Processing Systems (NeurIPS)

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment.

By: Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick

December 10, 2019

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

Neural Information Processing Systems (NeurIPS)

The success of lottery ticket initializations [7] suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these “winning ticket” initializations is computationally expensive. One potential solution is to reuse the same winning tickets across a variety of datasets and optimizers. However, the generality of winning ticket initializations remains unclear. Here, we attempt to answer this question by generating winning tickets for one training configuration (optimizer and dataset) and evaluating their performance on another configuration.

By: Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

December 10, 2019

Robust Multi-agent Counterfactual Prediction

Neural Information Processing Systems (NeurIPS)

We consider the problem of using logged data to make predictions about what would happen if we changed the ‘rules of the game’ in a multi-agent system. This task is difficult because in many cases we observe actions individuals take but not their private information or their full reward functions. In addition, agents are strategic, so when the rules change, they will also change their actions.

By: Alexander Peysakhovich, Christian Kroer, Adam Lerer

December 10, 2019

Limiting Extrapolation in Linear Approximate Value Iteration

Neural Information Processing Systems (NeurIPS)

We study linear approximate value iteration (LAVI) with a generative model. While linear models may accurately represent the optimal value function using a few parameters, several empirical and theoretical studies show the combination of least-squares projection with the Bellman operator may be expansive, thus leading LAVI to amplify errors over iterations and eventually diverge. We introduce an algorithm that approximates value functions by combining Q-values estimated at a set of anchor states.

By: Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill

December 9, 2019

Cold Case: the Lost MNIST Digits

Neural Information Processing Systems (NeurIPS)

Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc.

By: Chhavi Yadav, Leon Bottou