3 citations · 4 across the 4 of their papers we have counts for
5 papers
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility
Sambit Ghosh, Madelyn Mathai, R. Venkatesh Babu +1
Sparse autoencoders (SAEs) are typically trained on hundreds of millions to billions of activation tokens, yet little work has tested whether this scale is necessary. We study how…
Rethinking Dataset Distillation: Hard Truths about Soft Labels
Priyam Dey, Aditya Sahdev, Sunny Bhati +2
Despite the perceived success of large-scale dataset distillation (DD) methods, recent evidence finds that simple random image baselines perform on-par with state-of-theart DD meth…
ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations
Sravanti Addepalli, Priyam Dey, R. Venkatesh Babu
The need for abundant labelled data in supervised Adversarial Training (AT) has prompted the use of Self-Supervised Learning (SSL) techniques with AT. However, the direct applicati…
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri +4
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain. However,…
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
Sravanti Addepalli, Ashish Ramayee Asokan, Lakshay Sharma +1
Vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs, resulting in remarkable generalization across several data distributions. However, in s…