5.7k citations · 17.6k across the 75 of their papers we have counts for
10 papers · 1 filter
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle +1
We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial…
Dynamic Capacity Networks
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans +3
We introduce the Dynamic Capacity Network (DCN), a neural network that can adaptively assign its capacity across different portions of the input data. This is achieved by combining…
Within-Brain Classification for Brain Tumor Segmentation
Mohammad Havaei, Hugo Larochelle, Philippe Poulin +1
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a mach…
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan +5
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is di…
Brain Tumor Segmentation with Deep Neural Networks
Mohammad Havaei, Axel Davy, David Warde-Farley +6
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and…
Correlational Neural Networks
Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle +1
Common Representation Learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, is receiving a lot of attention recently. Two popular…
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research
Atousa Torabi, Christopher Pal, Hugo Larochelle +1
In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on…
Describing Videos by Exploiting Temporal Structure
Li Yao, Atousa Torabi, Kyunghyun Cho +4
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are…
MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray +1
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neu…
An Infinite Restricted Boltzmann Machine
Marc-Alexandre Côté, Hugo Larochelle
We present a mathematical construction for the restricted Boltzmann machine (RBM) that doesn't require specifying the number of hidden units. In fact, the hidden layer size is adap…