5 papers
Shortcut Invariance: Targeted Jacobian Regularization in Disentangled Latent Space
Shivam Pal, Sakshi Varshney, Piyush Rai
Deep neural networks are prone to learning shortcuts, spurious correlations present in the training data that undermine out-of-distribution (OOD) generalization. Most prior work mi…
NOVO: Unlearning-Compliant Vision Transformers
Soumya Roy, Soumya Banerjee, Vinay Verma +3
Machine unlearning (MUL) refers to the problem of making a pre-trained model selectively forget some training instances or class(es) while retaining performance on the remaining da…
Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture
Malay Pandey, Vaishali Jain, Nimit Godhani +2
In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial di…
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization
Shivam Pal, Aishwarya Gupta, Saqib Sarwar +1
Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement…
Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage
Aishwarya Gupta, Indranil Saha, Piyush Rai
Rigorous testing of machine learning models is necessary for trustworthy deployments. We present a novel black-box approach for generating test-suites for robust testing of deep ne…