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07/21/2021
Memorization in Deep Neural Networks: Does the Loss Function matter?
Deep Neural Networks, often owing to the overparameterization, are shown...
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06/29/2021
Adaptive Sample Selection for Robust Learning under Label Noise
Deep Neural Networks (DNNs) have been shown to be susceptible to memoriz...
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02/11/2021
Learning Gaussian-Bernoulli RBMs using Difference of Convex Functions Optimization
The Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM) is a useful...
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04/22/2019
PLUME: Polyhedral Learning Using Mixture of Experts
In this paper, we propose a novel mixture of expert architecture for lea...
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04/01/2019
Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application
In this paper we address the problem of discovering a small set of frequ...
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10/25/2018
Efficient Learning of Restricted Boltzmann Machines Using Covariance estimates
Learning of RBMs using standard algorithms such as CD(k) involves gradie...
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12/27/2017
Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from ...
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09/21/2017
Learning RBM with a DC programming Approach
By exploiting the property that the RBM log-likelihood function is the d...
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10/08/2015
Empirical Analysis of Sampling Based Estimators for Evaluating RBMs
The Restricted Boltzmann Machines (RBM) can be used either as classifier...
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07/08/2011
Polyceptron: A Polyhedral Learning Algorithm
In this paper we propose a new algorithm for learning polyhedral classif...
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06/08/2010
