
Memorization in Deep Neural Networks: Does the Loss Function matter?
Deep Neural Networks, often owing to the overparameterization, are shown...
read it

Adaptive Sample Selection for Robust Learning under Label Noise
Deep Neural Networks (DNNs) have been shown to be susceptible to memoriz...
read it

Learning GaussianBernoulli RBMs using Difference of Convex Functions Optimization
The GaussianBernoulli restricted Boltzmann machine (GBRBM) is a useful...
read it

PLUME: Polyhedral Learning Using Mixture of Experts
In this paper, we propose a novel mixture of expert architecture for lea...
read it

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...
read it

Efficient Learning of Restricted Boltzmann Machines Using Covariance estimates
Learning of RBMs using standard algorithms such as CD(k) involves gradie...
read it

Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from ...
read it

Learning RBM with a DC programming Approach
By exploiting the property that the RBM loglikelihood function is the d...
read it

Empirical Analysis of Sampling Based Estimators for Evaluating RBMs
The Restricted Boltzmann Machines (RBM) can be used either as classifier...
read it

Polyceptron: A Polyhedral Learning Algorithm
In this paper we propose a new algorithm for learning polyhedral classif...
read it

Efficient Discovery of Large Synchronous Events in Neural Spike Streams
We address the problem of finding patterns from multineuronal spike tra...
read it
P. S. Sastry
is this you? claim profile