no code implementations • 8 Mar 2024 • Varun Babbar, Zhicheng Guo, Cynthia Rudin
The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges.
no code implementations • 17 Feb 2023 • Xiaoying Zhi, Varun Babbar, Pheobe Sun, Fran Silavong, Ruibo Shi, Sean Moran
Our method enables pruning and training simultaneously, which saves energy in both the training and inference phases and avoids extra computational overhead from gating modules at inference time.
1 code implementation • 19 Aug 2022 • Agathe Lherondelle, Varun Babbar, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran
This paper presents Topical, a novel deep neural network for repository level embeddings.
no code implementations • 3 May 2022 • Varun Babbar, Umang Bhatt, Adrian Weller
We explore how such prediction sets impact expert decision-making in human-AI teams.
no code implementations • 25 Mar 2022 • Antonios Georgiadis, Varun Babbar, Fran Silavong, Sean Moran, Rob Otter
We demonstrate that the widely varying data quality on FL client nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image segmentation.
no code implementations • 26 Mar 2021 • Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu, Varun Babbar, Rafal K. Mantiuk
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution.