166 citations · 1.4k across the 47 of their papers we have counts for
6 papers · 1 filter
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes +2
Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pair…
Combating Label Noise in Deep Learning Using Abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes +2
We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby…
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
Sunil Thulasidasan, Jeffrey Bilmes
We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a gr…
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
Sunil Thulasidasan, Jeffrey Bilmes, Garrett Kenyon
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or di…
Stream Clipper: Scalable Submodular Maximization on Stream
Tianyi Zhou, Jeff Bilmes
We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds…
Divide-and-Conquer Learning by Anchoring a Conical Hull
Tianyi Zhou, Jeff Bilmes, Carlos Guestrin
We reduce a broad class of machine learning problems, usually addressed by EM or sampling, to the problem of finding the extremal rays spanning the conical hull of a data point…