50 citations · 389 across the 39 of their papers we have counts for
4 papers · 1 filter
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
Shikhar Tuli, Chia-Hao Li, Ritvik Sharma +1
Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most c…
CTRL: Clustering Training Losses for Label Error Detection
Chang Yue, Niraj K. Jha
In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models…
SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare
Bhishma Dedhia, Roshini Balasubramanian, Niraj K. Jha
The Synthetic Control method has pioneered a class of powerful data-driven techniques to estimate the counterfactual reality of a unit from donor units. At its core, the technique…
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
Shikhar Tuli, Bhishma Dedhia, Shreshth Tuli +1
The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based m…