
The BreakEven Point on Optimization Trajectories of Deep Neural Networks
The early phase of training of deep neural networks is critical for thei...
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Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
We introduce a parameterization method called Neural Bayes which allows ...
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Entropy Penalty: Towards Generalization Beyond the IID Assumption
It has been shown that instead of learning actual object features, deep ...
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How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
Residual networks (ResNet) and weight normalization play an important ro...
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The Benefits of Overparameterization at Initialization in Deep ReLU Networks
It has been noted in existing literature that overparameterization in R...
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hdetach: Modifying the LSTM Gradient Towards Better Optimization
Recurrent neural networks are known for their notorious exploding and va...
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On the Spectral Bias of Deep Neural Networks
It is well known that overparametrized deep neural networks (DNNs) are ...
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A Walk with SGD
Exploring why stochastic gradient descent (SGD) based optimization metho...
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Variational BiLSTMs
Recurrent neural networks like long shortterm memory (LSTM) are importa...
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Three Factors Influencing Minima in SGD
We study the properties of the endpoint of stochastic gradient descent (...
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Fraternal Dropout
Recurrent neural networks (RNNs) are important class of architectures am...
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Residual Connections Encourage Iterative Inference
Residual networks (Resnets) have become a prominent architecture in deep...
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A Closer Look at Memorization in Deep Networks
We examine the role of memorization in deep learning, drawing connection...
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On Optimality Conditions for AutoEncoder Signal Recovery
AutoEncoders are unsupervised models that aim to learn patterns from ob...
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Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
While the authors of Batch Normalization (BN) identify and address an im...
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Why Regularized AutoEncoders learn Sparse Representation?
While the authors of Batch Normalization (BN) identify and address an im...
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Dimensionality Reduction with Subspace Structure Preservation
Modeling data as being sampled from a union of independent subspaces has...
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Is Joint Training Better for Deep AutoEncoders?
Traditionally, when generative models of data are developed via deep arc...
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An Analysis of Random Projections in Cancelable Biometrics
With increasing concerns about security, the need for highly secure phys...
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Devansh Arpit
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Postdoctoral Fellow at University of Montreal  Montreal Institute for Learning Algorithms (MILA)