5 papers
SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments
Suryanarayana Reddy Yarrabothula, Manisha Chawla, Kunal Sinha +5
Existing video benchmarks evaluate action recognition on consumer videos, egocentric recordings, or simulated industrial environments. They do not test vision-language models under…
AdaGradSelect: An adaptive gradient-guided layer selection method for efficient fine-tuning of SLMs
Anshul Kumar, Gagan Raj Gupta, Manisha Chawla
Large Language Models (LLMs) can perform many NLP tasks well, but fully fine-tuning them is expensive and requires a lot of memory. Parameter-Efficient Fine-Tuning (PEFT) methods s…
PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels
Aayushman, Hemanth Gaddey, Vidhi Mittal +2
Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions needs to be addressed before deploy…
Entropy Aware Training for Fast and Accurate Distributed GNN
Dhruv Deshmukh, Gagan Raj Gupta, Manisha Chawla +2
Several distributed frameworks have been developed to scale Graph Neural Networks (GNNs) on billion-size graphs. On several benchmarks, we observe that the graph partitions generat…
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients
Manas Wadhwa, Gagan Raj Gupta, Ashutosh Sahu +2
The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices…