Jeff A. Bilmes - Citegraph

Author Info

Name	Affiliation	Papers
JEFF A. BILMES	Dept. of Electrical Engineering, Univ. of Washington, Seattle, WA	34
Collaborators	Citations	PageRank
40	278	16.88
Referers	Referees	References
738	759	525

Search Limit

100759

Publications (34 rows)

Collaborators (40 rows)

Referers (100 rows)

Referees (100 rows)

Title	Citations	PageRank	Year
Combating Label Noise in Deep Learning Using Abstention.	4	0.38	2019
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs	0	0.34	2019
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation.	0	0.34	2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks.	7	0.46	2019
Choosing Non-redundant Representative Subsets Of Protein Sequence Data Sets Using Submodular Optimization.	1	0.35	2018
Minimax Curriculum Learning - Machine Teaching with Desirable Difficulties and Scheduled Diversity.	1	0.35	2018
Scaling Submodular Maximization via Pruned Submodularity Graphs.	1	0.37	2017
Graph cuts with interacting edge weights: examples, approximations, and algorithms.	1	0.35	2017
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty	1	0.37	2017
Speech Production in Speech Technologies: Introduction to the CSL Special Issue	1	0.36	2016
Stream Clipper: Scalable Submodular Maximization on Stream.	0	0.34	2016
Algorithms for Optimizing the Ratio of Submodular Functions.	2	0.40	2016
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization.	0	0.34	2016
Bipartite matching generalizations for peptide identification in tandem mass spectrometry.	0	0.34	2016
On Deep Multi-View Representation Learning: Objectives and Optimization.	5	0.43	2016
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs.	0	0.34	2016
Faster and more accurate graphical model identification of tandem mass spectra using trellises.	0	0.34	2016
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications	1	0.36	2015
On Approximate Non-submodular Minimization via Tree-Structured Supermodularity.	2	0.38	2015
On Deep Multi-View Representation Learning	87	1.94	2015
Entropic Graph-based Posterior Regularization	1	0.35	2015
Submodularity in Data Subset Selection and Active Learning	31	0.98	2015
Svitchboard Ii And Fisver I: High-Quality Limited-Complexity Corpora Of Conversational English Speech	3	0.39	2015
Submodular Hamming Metrics.	0	0.34	2015
Polyhedral aspects of Submodularity, Convexity and Concavity.	3	0.40	2015
Submodular Point Processes with Applications to Machine learning.	6	0.46	2015
Unsupervised Learning Of Acoustic Features Via Deep Canonical Correlation Analysis	31	1.08	2015
Learning Mixtures of Submodular Functions for Image Collection Summarization.	42	1.02	2014
Fast Multi-stage Submodular Maximization.	4	0.45	2014
Divide-and-Conquer Learning by Anchoring a Conical Hull.	6	0.48	2014
Submodularity for Data Selection in Machine Translation.	15	0.56	2014
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking.	7	0.51	2014
Monotone Closure Of Relaxed Constraints In Submodular Optimization: Connections Between Minimization And Maximization	5	0.43	2014
Unsupervised submodular subset selection for speech data	10	0.56	2014