Search Results for author: Jeffrey Bilmes

Found 7 papers, 2 papers with code

apricot: Submodular selection for data summarization in Python

1 code implementation8 Jun 2019 Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble

This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets.

Data Summarization

Knows When it Doesn’t Know: Deep Abstaining Classifiers

no code implementations ICLR 2019 Sunil Thulasidasan, Tanmoy Bhattacharya, Jeffrey Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof

We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.

Deep Submodular Functions

no code implementations31 Jan 2017 Jeffrey Bilmes, Wenruo Bai

Lastly, we discuss strategies to learn DSFs, and define the classes of deep supermodular functions, deep difference of submodular functions, and deep multivariate submodular functions, and discuss where these can be useful in applications.


Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs

no code implementations15 Dec 2016 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 distributed setting.

Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization

no code implementations15 Dec 2016 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 graph induced by the data.

General Classification

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