activity
20192022
most citedDeep Neural Networks with Trainable Activations and Controlled Lipschitz Constant

31 citations · 47 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ML2022★ 9 cited

Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems

Shailesh Garg, Harshit Gupta, Souvik Chakraborty

Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates consi…

eess.IV2021

End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data

Youssef S. G. Nashed, Frederic Poitevin, Harshit Gupta +4

Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations. Here, we present an end-to-end unsupervised approac…

cs.LG2020★ 31 cited

Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant

Shayan Aziznejad, Harshit Gupta, Joaquim Campos +1

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound…

cs.CL2019★ 7 cited

Improving Document Classification with Multi-Sense Embeddings

Vivek Gupta, Ankit Saw, Pegah Nokhiz +2

Efficient representation of text documents is an important building block in many NLP tasks. Research on long text categorization has shown that simple weighted averaging of word v…

eess.IV2019

Time-Dependent Deep Image Prior for Dynamic MRI

Jaejun Yoo, Kyong Hwan Jin, Harshit Gupta +3

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study…