A. Jaiswal
10 Papers
3 Citations
A. Jaiswal is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 8 publications.
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Papers
Further divided gender gaps in research productivity and collaboration during the COVID-19 pandemic: Evidence from coronavirus-related literature
TL;DR: In this paper , the authors explored the evolution of gender inequalities before and during the COVID-19 pandemic by comparing the differences in the numbers and shares of authorships, leadership in publications, gender composition of collaboration, and scientific impacts.
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CancerGPT for few shot drug pair synergy prediction using large pretrained language models
Tianhao Li,Sandesh Krishna Shetty,Advaith Kamath,A. Jaiswal,Xiaoqian Jiang,Ying Ding,Yejin Kim +6 more
TL;DR: The experiments, which involved seven rare tissues from different cancer types, demonstrate that the LLM-based prediction model achieves significant accuracy with very few or zero samples, and contributes to tackling drug pair synergy prediction in rare tissues with limited data.
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Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
TL;DR: In this article , Instant Soup Pruning (ISP) is proposed to generate lottery ticket quality subnetworks by replacing the expensive intermediate pruning stages of IMP with computationally efficient weak mask generation and aggregation routine.
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RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
A. Jaiswal,Kumar Ashutosh,Justin F. Rousseau,Yifan Peng,Zhangyang Wang,Ying Ding +5 more
- 15 Oct 2022
TL;DR: A Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information is proposed.
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How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
Gregory Holste,Ziyu Jiang,A. Jaiswal,M. Hanna,Shlomo Minkowitz,Alan C. Legasto,Joanna G. Escalon,Sharon Steinberger,Mark E. Bittman,Thomas C. Shen,Ying Ding,Ronald M. Summers,George Shih,Yifan Peng,Zhangyang Wang +14 more
TL;DR: This work performs the first analysis of pruning’s effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs) on two large CXR datasets, and finds that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty.
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