14.5k citations · 90.1k across the 347 of their papers we have counts for
31 papers · 1 filter
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…
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio
Recurrent neural networks (RNNs) are notoriously difficult to train. When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes…
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…
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…
Denoising Criterion for Variational Auto-Encoding Framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic +1
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injec…