Zobrazeno 1 - 10
of 5 193
pro vyhledávání: '"P. Jayashree"'
Autor:
Faiza Iqbal, Prashant Chandra, Aakif Ashar Khan, Leslie Edward S Lewis, Dinesh Acharya, K.E. Vandana, P. Jayashree, Padmaja A. Shenoy
Publikováno v:
Clinical Epidemiology and Global Health, Vol 24, Iss , Pp 101414- (2023)
Introduction: The use of machine learning (ML) methods can help clinicians predict neonatal sepsis better. Predicting mortality due to sepsis is essential for benchmarking and assessing NICU healthcare services. Methodology: The newborn records of th
Externí odkaz:
https://doaj.org/article/bdf37323bab44781a997fb3e541083a0
In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional $L_{1}$ regression loss tends to bias towards larger boxes, as they disproportionately contribute more towards the overall loss compared to smalle
Externí odkaz:
http://arxiv.org/abs/2410.22638
Autor:
Kamath, Aditya K, Prabhu, Ramya, Mohan, Jayashree, Peter, Simon, Ramjee, Ramachandran, Panwar, Ashish
Each request in LLM inference goes through two phases: compute-bound prefill and memory-bandwidth-bound decode. To improve GPU utilization, recent systems use hybrid batching that combines the prefill and decode phases of different requests into the
Externí odkaz:
http://arxiv.org/abs/2410.18038
Autor:
Kagaya, Tomoyuki, Lou, Yuxuan, Yuan, Thong Jing, Lakshmi, Subramanian, Karlekar, Jayashree, Pranata, Sugiri, Murakami, Natsuki, Kinose, Akira, Oguri, Koki, Wick, Felix, You, Yang
In recent years, Large Language Models (LLMs) have demonstrated high reasoning capabilities, drawing attention for their applications as agents in various decision-making processes. One notably promising application of LLM agents is robotic manipulat
Externí odkaz:
http://arxiv.org/abs/2410.16919
`Extreme Classification'' (or XC) is the task of annotating data points (queries) with relevant labels (documents), from an extremely large set of $L$ possible labels, arising in search and recommendations. The most successful deep learning paradigm
Externí odkaz:
http://arxiv.org/abs/2409.20156
Autor:
Idnay, Betina, Xu, Zihan, Adams, William G., Adibuzzaman, Mohammad, Anderson, Nicholas R., Bahroos, Neil, Bell, Douglas S., Bumgardner, Cody, Campion, Thomas, Castro, Mario, Cimino, James J., Cohen, I. Glenn, Dorr, David, Elkin, Peter L, Fan, Jungwei W., Ferris, Todd, Foran, David J., Hanauer, David, Hogarth, Mike, Huang, Kun, Kalpathy-Cramer, Jayashree, Kandpal, Manoj, Karnik, Niranjan S., Katoch, Avnish, Lai, Albert M., Lambert, Christophe G., Li, Lang, Lindsell, Christopher, Liu, Jinze, Lu, Zhiyong, Luo, Yuan, McGarvey, Peter, Mendonca, Eneida A., Mirhaji, Parsa, Murphy, Shawn, Osborne, John D., Paschalidis, Ioannis C., Harris, Paul A., Prior, Fred, Shaheen, Nicholas J., Shara, Nawar, Sim, Ida, Tachinardi, Umberto, Waitman, Lemuel R., Wright, Rosalind J., Zai, Adrian H., Zheng, Kai, Lee, Sandra Soo-Jin, Malin, Bradley A., Natarajan, Karthik, Price II, W. Nicholson, Zhang, Rui, Zhang, Yiye, Xu, Hua, Bian, Jiang, Weng, Chunhua, Peng, Yifan
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) P
Externí odkaz:
http://arxiv.org/abs/2410.12793
Autor:
Bawali, Biplab, Chowdhury, Shubhadip, Mukherjee, Smita, Giglia, Angelo, Mahne, Nicola, Nannarone, Stefano, Mukhopadhyay, Mrinmay, Saha, Jayashree, Datta, Alokmay
The ion-lipid interface in Langmuir monolayers of Dipalmitoylphosphatidylcholine (DPPC) on pure water and 10 mM solutions of Na+ and K+ at different [K+]/[Na+] (a), atom/atom ratios, were studied initially by Surface Pressure (p) versus Specific Mole
Externí odkaz:
http://arxiv.org/abs/2409.19122
We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using the predictive semi-recurrent neural network outlined in Chittenden and Tojeiro (2023), and
Externí odkaz:
http://arxiv.org/abs/2409.16079
Optimised neural network predictions of galaxy formation histories using semi-stochastic corrections
We present a novel methodology to improve neural network (NN) predictions of galaxy formation histories by incorporating semi-stochastic corrections to account for short-timescale variability. Our paper addresses limitations in existing models that c
Externí odkaz:
http://arxiv.org/abs/2409.16548
Autor:
Agrawal, Amey, Agarwal, Anmol, Kedia, Nitin, Mohan, Jayashree, Kundu, Souvik, Kwatra, Nipun, Ramjee, Ramachandran, Tumanov, Alexey
Serving large language models (LLMs) in production can incur substantial costs, which has prompted recent advances in inference system optimizations. Today, these systems are evaluated against conventional latency and throughput metrics (eg. TTFT, TB
Externí odkaz:
http://arxiv.org/abs/2407.07000