35 citations · 79 across the 12 of their papers we have counts for
13 papers
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +4
Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these s…
Isometric Graph Neural Networks
Matthew Walker, Bo Yan, Yiou Xiao +2
Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph. Geometric techniques to ext…
Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices
Ayan Acharya, Joydeep Ghosh, Mingyuan Zhou
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a nov…
Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning
Ayan Acharya, Eduardo R. Hruschka, Joydeep Ghosh +2
Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share…
An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers
Ayan Acharya, Eduardo R. Hruschka, Joydeep Ghosh +1
Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share the same class…