347 citations · 588 across the 49 of their papers we have counts for
26 papers · 1 filter
DifCluE: Generating Counterfactual Explanations with Diffusion Autoencoders and modal clustering
Suparshva Jain, Amit Sangroya, Lovekesh Vig
Generating multiple counterfactual explanations for different modes within a class presents a significant challenge, as these modes are distinct yet converge under the same classif…
Can Physics Informed Neural Operators Self Improve?
Ritam Majumdar, Amey Varhade, Shirish Karande +1
Self-training techniques have shown remarkable value across many deep learning models and tasks. However, such techniques remain largely unexplored when considered in the context o…
How important are specialized transforms in Neural Operators?
Ritam Majumdar, Shirish Karande, Lovekesh Vig
Simulating physical systems using Partial Differential Equations (PDEs) has become an indispensible part of modern industrial process optimization. Traditionally, numerical solvers…
HyperLoRA for PDEs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have t…
DeepEpiSolver: Unravelling Inverse problems in Covid, HIV, Ebola and Disease Transmission
Ritam Majumdar, Shirish Karande, Lovekesh Vig
The spread of many infectious diseases is modeled using variants of the SIR compartmental model, which is a coupled differential equation. The coefficients of the SIR model determi…
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2
We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, o…