2 citations · 3 across the 6 of their papers we have counts for
6 papers
Unlearning in Diffusion models under Data Constraints: A Variational Inference Approach
Subhodip Panda, Varun M S, Shreyans Jain +2
For a responsible and safe deployment of diffusion models in various domains, regulating the generated outputs from these models is desirable because such models could generate und…
LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation
Piyush Tiwary, Kinjawl Bhattacharyya, Prathosh A. P
Medical image segmentation models often struggle to generalize across different domains due to various reasons. Domain Generalization (DG) methods overcome this either through repr…
FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models
Subhodip Panda, Prathosh AP
The heightened emphasis on the regulation of deep generative models, propelled by escalating concerns pertaining to privacy and compliance with regulatory frameworks, underscores t…
ScRAE: Deterministic Regularized Autoencoders with Flexible Priors for Clustering Single-cell Gene Expression Data
Arnab Kumar Mondal, Himanshu Asnani, Parag Singla +1
Clustering single-cell RNA sequence (scRNA-seq) data poses statistical and computational challenges due to their high-dimensionality and data-sparsity, also known as `dropout' even…
To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs
Arnab Kumar Mondal, Himanshu Asnani, Parag Singla +1
Regularized Auto-Encoders (RAEs) form a rich class of neural generative models. They effectively model the joint-distribution between the data and the latent space using an Encoder…
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation
Arnab Kumar Mondal, Arnab Bhattacharya, Sudipto Mukherjee +3
Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications. N…