7.4k citations · 24.2k across the 136 of their papers we have counts for
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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…
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Francesco Visin, Marco Ciccone, Adriana Romero +5
We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks…
Variance Reduction in SGD by Distributed Importance Sampling
Guillaume Alain, Alex Lamb, Chinnadhurai Sankar +2
Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for di…
Task Loss Estimation for Sequence Prediction
Dzmitry Bahdanau, Dmitriy Serdyuk, Philémon Brakel +4
Often, the performance on a supervised machine learning task is evaluated with a emph{task loss} function that cannot be optimized directly. Examples of such loss functions include…
Delving Deeper into Convolutional Networks for Learning Video Representations
Nicolas Ballas, Li Yao, Chris Pal +1
We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs).…
Deconstructing the Ladder Network Architecture
Mohammad Pezeshki, Linxi Fan, Philemon Brakel +2
The Manual labeling of data is and will remain a costly endeavor. For this reason, semi-supervised learning remains a topic of practical importance. The recently proposed Ladder Ne…