194 citations · 270 across the 13 of their papers we have counts for
5 papers · 1 filter
Minibatching Offers Improved Generalization Performance for Second Order Optimizers
Eric Silk, Swarnita Chakraborty, Nairanjana Dasgupta +3
Training deep neural networks (DNNs) used in modern machine learning is computationally expensive. Machine learning scientists, therefore, rely on stochastic first-order methods fo…
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
Andrew Engel, Zhichao Wang, Natalie S. Frank +4
A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend…
TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models
Andrew Engel, Zhichao Wang, Anand D. Sarwate +2
We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework. We provide an efficient method to…
Distributed Differentially Private Computation of Functions with Correlated Noise
Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate
Many applications of machine learning, such as human health research, involve processing private or sensitive information. Privacy concerns may impose significant hurdles to collab…
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate +1
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to…