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5 papers · 1 filter
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
Traditional and Heavy-Tailed Self Regularization in Neural Network Models
Charles H. Martin, Michael W. Mahoney
Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Incep…
Semi-supervised Eigenvectors for Large-scale Locally-biased Learning
Toke J. Hansen, Michael W. Mahoney
In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to per…
Probabilistic Latent Semantic Analysis
Thomas Hofmann
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and fil…
Multi-View Learning in the Presence of View Disagreement
C. Christoudias, Raquel Urtasun, Trevor Darrell
Traditional multi-view learning approaches suffer in the presence of view disagreement,i.e., when samples in each view do not belong to the same class due to view corruption, occlu…