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pro vyhledávání: '"Beigi A"'
Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate pri
Externí odkaz:
http://arxiv.org/abs/2411.07317
A quantum analogue of the Central Limit Theorem (CLT), first introduced by Cushen and Hudson (1971), states that the $n$-fold convolution $\rho^{\boxplus n}$ of an $m$-mode quantum state $\rho$ with zero first moments and finite second moments conver
Externí odkaz:
http://arxiv.org/abs/2410.21998
Autor:
Beigi, Mohammad, Wang, Sijia, Shen, Ying, Lin, Zihao, Kulkarni, Adithya, He, Jianfeng, Chen, Feng, Jin, Ming, Cho, Jin-Hee, Zhou, Dawei, Lu, Chang-Tien, Huang, Lifu
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current metho
Externí odkaz:
http://arxiv.org/abs/2410.20199
Autor:
Beigi, Alimohammad, Jiang, Bohan, Li, Dawei, Kumarage, Tharindu, Tan, Zhen, Shaeri, Pouya, Liu, Huan
Human fact-checkers have specialized domain knowledge that allows them to formulate precise questions to verify information accuracy. However, this expert-driven approach is labor-intensive and is not scalable, especially when dealing with complex mu
Externí odkaz:
http://arxiv.org/abs/2410.04616
Autor:
Liu, Xing Yi, Beigi, Homayoon
Publikováno v:
Recognition Technologies, Inc. Technical Report, 2024
Presently, punctuation restoration models are evaluated almost solely on well-structured, scripted corpora. On the other hand, real-world ASR systems and post-processing pipelines typically apply towards spontaneous speech with significant irregulari
Externí odkaz:
http://arxiv.org/abs/2409.11241
Analyzing data from past clinical trials is part of the ongoing effort to optimize the design, implementation, and execution of new clinical trials and more efficiently bring life-saving interventions to market. While there have been recent advances
Externí odkaz:
http://arxiv.org/abs/2409.07089
Autor:
Beigi, Majed Valad, Cao, Yi, Gurumurthi, Sudhanva, Recchia, Charles, Walton, Andrew, Sridharan, Vilas
This paper is a corrigendum to the paper by Beigi et al. published at HPCA 2023 https://doi.org/10.1109/HPCA56546.2023.10071066. The HPCA paper presented a detailed field data analysis of faults observed at scale in DDR4 DRAM from two different memor
Externí odkaz:
http://arxiv.org/abs/2408.15302
Autor:
Ter-Avanesov, Boris, Beigi, Homayoon
We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural network (RNN) models, and a time-delay neural network (TDNN) for
Externí odkaz:
http://arxiv.org/abs/2409.06724
Model attribution for LLM-generated disinformation poses a significant challenge in understanding its origins and mitigating its spread. This task is especially challenging because modern large language models (LLMs) produce disinformation with human
Externí odkaz:
http://arxiv.org/abs/2407.21264
Autor:
Beigi, Mohammad, Shen, Ying, Yang, Runing, Lin, Zihao, Wang, Qifan, Mohan, Ankith, He, Jianfeng, Jin, Ming, Lu, Chang-Tien, Huang, Lifu
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations. Addressing this challenge, our research introduces InternalInspe
Externí odkaz:
http://arxiv.org/abs/2406.12053