1 citations · 1 across the 4 of their papers we have counts for
4 papers
Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer
Nilushika Udayangani, Kishor Nandakishor, Marimuthu Palaniswami
While traditional time-series classifiers assume full sequences at inference, practical constraints (latency and cost) often limit inputs to partial prefixes. The absence of class-…
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
Chathura Wimalasiri, Yuchong Yao, Kishor Nandakishor +1
Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabe…
MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification
Nilushika Udayangani, Kishor Nandakishor, Marimuthu Palaniswami
Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time series data analysis. These models…
Learning to Reason: Temporal Saliency Distillation for Interpretable Knowledge Transfer
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu Palaniswami
Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Curren…