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Adversarially Robust Learning via Entropic Regularization
Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury +2
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additi…
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Sergio Botelho, Ameya Joshi, Biswajit Khara +4
Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equation…
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery
Minsu Cho, Mohammadreza Soltani, Chinmay Hegde
In this paper, we study two important problems in the automated design of neural networks -- Hyper-parameter Optimization (HPO), and Neural Architecture Search (NAS) -- through the…
ESPN: Extremely Sparse Pruned Networks
Minsu Cho, Ameya Joshi, Chinmay Hegde
Deep neural networks are often highly overparameterized, prohibiting their use in compute-limited systems. However, a line of recent works has shown that the size of deep networks…