Filter by Research Area
Filter by Research Area
Year Published

239 Results

October 31, 2018

Neural Compositional Denotational Semantics for Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics.

By: Nitish Gupta, Mike Lewis
October 31, 2018

Semantic Parsing for Task Oriented Dialog using Hierarchical Representations

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on…

By: Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis
October 26, 2018

SING: Symbol-to-Instrument Neural Generator

Conference on Neural Information Processing Systems (NIPS)

In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present SING, a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity.

By: Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Leon Bottou, Francis Bach
September 10, 2018

Value-aware Quantization for Training and Inference of Neural Networks

European Conference on Computer Vision (ECCV)

We propose a novel value-aware quantization which applies aggressively reduced precision to the majority of data while separately handling a small amount of large values in high precision, which reduces total quantization errors under very low precision.

By: Eunhyeok Park, Sungjoo Yoo, Peter Vajda
September 10, 2018

Predicting Future Instance Segmentation by Forecasting Convolutional Features

European Conference on Computer Vision (ECCV)

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects.

By: Pauline Luc, Camille Couprie, Yann LeCun, Jakob Verbeek
September 9, 2018

Multi-Fiber Networks for Video Recognition

European Conference on Computer Vision (ECCV)

In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks.

By: Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng
September 9, 2018

Deep Clustering for Unsupervised Learning of Visual Features

European Conference on Computer Vision (ECCV)

In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.

By: Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze
September 9, 2018

Memory Aware Synapses: Learning what (not) to forget

European Conference on Computer Vision (ECCV)

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively.

By: Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars
September 9, 2018

Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance

European Conference on Computer Vision (ECCV)

Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects – forming a “dictionary” of concepts acquired through the learning process. In this work we introduce a simple, efficient zero-shot learning approach based on this observation.

By: Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, Stefan Lee
September 9, 2018

Graph R-CNN for Scene Graph Generation

European Conference on Computer Vision (ECCV)

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images.

By: Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh