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Found 12 papers, 3 papers with code
Memorization in Deep Neural Networks: Does the Loss Function matter?
Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data.
Adaptive Sample Selection for Robust Learning under Label Noise
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data.
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Image Classification
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Image Classification
Image Classification with Label Noise
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PLUME: Polyhedral Learning Using Mixture of Experts
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers.
Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application
By considering text documents as temporal sequences of words, the data mining algorithm can find a set of characteristic episodes for all the training data as a whole.
Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates
Learning RBMs using standard algorithms such as CD(k) involves gradient descent on the negative log-likelihood.
Robust Loss Functions under Label Noise for Deep Neural Networks
For binary classification there exist theoretical results on loss functions that are robust to label noise.
Learning RBM with a DC programming Approach
By exploiting the property that the RBM log-likelihood function is the difference of convex functions, we formulate a stochastic variant of the difference of convex functions (DC) programming to minimize the negative log-likelihood.
On the Robustness of Decision Tree Learning under Label Noise
In most practical problems of classifier learning, the training data suffers from the label noise.
Empirical Analysis of Sampling Based Estimators for Evaluating RBMs
The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models.
Making Risk Minimization Tolerant to Label Noise
Through extensive empirical studies, we show that risk minimization under the $0-1$ loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.
K-Plane Regression
In this paper, we present a novel algorithm for piecewise linear regression which can learn continuous as well as discontinuous piecewise linear functions.
Polyceptron: A Polyhedral Learning Algorithm
In this paper we propose a new algorithm for learning polyhedral classifiers which we call as Polyceptron.
