Zobrazeno 1 - 10
of 18 671
pro vyhledávání: '"Mohammad, Amin A."'
Two-dimensional (2D) materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface to volume ratio and composition of low-cost, abundant elements. Whil
Externí odkaz:
http://arxiv.org/abs/2410.06876
Autor:
Roshani, Mohammad Amin, Zhou, Xiangyu, Qiang, Yao, Suresh, Srinivasan, Hicks, Steve, Sethuraman, Usha, Zhu, Dongxiao
Large language models (LLMs) have shown remarkable capabilities in various natural language tasks and are increasingly being applied in healthcare domains. This work demonstrates a new LLM-powered disease risk assessment approach via streaming human-
Externí odkaz:
http://arxiv.org/abs/2409.15027
In this paper, we extend the framework of quantitative information flow (QIF) to include adversaries that use Kolmogorov-Nagumo $f$-mean to infer secrets of a private system. Specifically, in our setting, an adversary uses Kolmogorov-Nagumo $f$-mean
Externí odkaz:
http://arxiv.org/abs/2409.04108
The combination of machine learning (ML) and sparsity-promoting techniques is enabling direct extraction of governing equations from data, revolutionizing computational modeling in diverse fields of science and engineering. The discovered dynamical m
Externí odkaz:
http://arxiv.org/abs/2409.04463
Autor:
Ghaffarzadeh-Esfahani, Mohammadreza, Ghaffarzadeh-Esfahani, Mahdi, Salahi-Niri, Arian, Toreyhi, Hossein, Atf, Zahra, Mohsenzadeh-Kermani, Amirali, Sarikhani, Mahshad, Tajabadi, Zohreh, Shojaeian, Fatemeh, Bagheri, Mohammad Hassan, Feyzi, Aydin, Tarighatpayma, Mohammadamin, Gazmeh, Narges, Heydari, Fateme, Afshar, Hossein, Allahgholipour, Amirreza, Alimardani, Farid, Salehi, Ameneh, Asadimanesh, Naghmeh, Khalafi, Mohammad Amin, Shabanipour, Hadis, Moradi, Ali, Zadeh, Sajjad Hossein, Yazdani, Omid, Esbati, Romina, Maleki, Moozhan, Nasr, Danial Samiei, Soheili, Amirali, Majlesi, Hossein, Shahsavan, Saba, Soheilipour, Alireza, Goudarzi, Nooshin, Taherifard, Erfan, Hatamabadi, Hamidreza, Samaan, Jamil S, Savage, Thomas, Sakhuja, Ankit, Soroush, Ali, Nadkarni, Girish, Darazam, Ilad Alavi, Pourhoseingholi, Mohamad Amin, Safavi-Naini, Seyed Amir Ahmad
Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materi
Externí odkaz:
http://arxiv.org/abs/2409.02136
This work investigates the application of Machine Unlearning (MU) for mitigating the impact of trojans embedded in conventional large language models of natural language (Text-LLMs) and large language models of code (Code-LLMs) We propose a novel unl
Externí odkaz:
http://arxiv.org/abs/2408.12416
This paper investigates the privacy funnel, a privacy-utility tradeoff problem in which mutual information quantifies both privacy and utility. The objective is to maximize utility while adhering to a specified privacy budget. However, the privacy fu
Externí odkaz:
http://arxiv.org/abs/2408.09659
This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex, a suite of resources designed to enhance LLMs' proficiency in p
Externí odkaz:
http://arxiv.org/abs/2408.08848
This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of lea
Externí odkaz:
http://arxiv.org/abs/2408.03451
Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of its tasks to the expert. Although there are currently systems that fo
Externí odkaz:
http://arxiv.org/abs/2407.12710