88 citations · 270 across the 23 of their papers we have counts for
25 papers
Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation
Tzu Ling Liu, Ian Stavness, Mrigank Rochan
Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled source domain to an unlabeled ta…
MoEKD: Mixture-of-Experts Knowledge Distillation for Robust and High-Performing Compressed Code Models
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Large language models for code have achieved strong performance across diverse software analytics tasks, yet their real-world adoption remains limited by high computational demands…
A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Transformer-based language models of code have achieved state-of-the-art performance across a wide range of software analytics tasks, but their practical deployment remains limited…
Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations
Zahidul Islam, Sujoy Paul, Mrigank Rochan
Existing video highlight detection methods, although advanced, struggle to generalize well to all test videos. These methods typically employ a generic highlight detection model fo…
Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Transformer-based language models for code have shown remarkable performance in various software analytics tasks, but their adoption is hindered by high computational costs, slow i…
Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet?
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Large Language Model (LLM)-generated data is increasingly used in software analytics, but it is unclear how this data compares to human-written data, particularly when models are e…