Zobrazeno 1 - 10
of 3 349
pro vyhledávání: '"Alinejad, A."'
The quality of instruction data directly affects the performance of fine-tuned Large Language Models (LLMs). Previously, \cite{li2023one} proposed \texttt{NUGGETS}, which identifies and selects high-quality quality data from a large dataset by identi
Externí odkaz:
http://arxiv.org/abs/2412.09990
Autor:
Wang, Qiyao, Ni, Shiwen, Liu, Huaren, Lu, Shule, Chen, Guhong, Feng, Xi, Wei, Chi, Qu, Qiang, Alinejad-Rokny, Hamid, Lin, Yuan, Yang, Min
As the capabilities of Large Language Models (LLMs) continue to advance, the field of patent processing has garnered increased attention within the natural language processing community. However, the majority of research has been concentrated on clas
Externí odkaz:
http://arxiv.org/abs/2412.09796
Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent structural alterations that influence genomic diversity and disease susceptibility. While coding regio
Externí odkaz:
http://arxiv.org/abs/2411.17956
Autor:
Shamsi, Afshar, Becirovic, Rejisa, Argha, Ahmadreza, Abbasnejad, Ehsan, Alinejad-Rokny, Hamid, Mohammadi, Arash
Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution, even when source data is inaccessible. While traditional TTA methods often rely on entropy as a confidence metric, its eff
Externí odkaz:
http://arxiv.org/abs/2409.09251
Autor:
Doan, Bao Gia, Shamsi, Afshar, Guo, Xiao-Yu, Mohammadi, Arash, Alinejad-Rokny, Hamid, Sejdinovic, Dino, Ranasinghe, Damith C., Abbasnejad, Ehsan
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non- Ba
Externí odkaz:
http://arxiv.org/abs/2407.20891
The rapid progress in Large Language Models (LLMs) has prompted the creation of numerous benchmarks to evaluate their capabilities.This study focuses on the Comprehensive Medical Benchmark in Chinese (CMB), showcasing how dataset diversity and distri
Externí odkaz:
http://arxiv.org/abs/2407.19705
Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display near-random performance on reasoning tasks. To address this, we introduce
Externí odkaz:
http://arxiv.org/abs/2407.18069
Question answering systems (QA) utilizing Large Language Models (LLMs) heavily depend on the retrieval component to provide them with domain-specific information and reduce the risk of generating inaccurate responses or hallucinations. Although the e
Externí odkaz:
http://arxiv.org/abs/2406.06458
Autor:
Rahmani, Amir Masoud, Khoshvaght, Parisa, Alinejad-Rokny, Hamid, Sadeghi, Samira, Asghari, Parvaneh, Arabi, Zohre, Hosseinzadeh, Mehdi
Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow and peripheral blood. Manual diagnosis of this disease is a tedious and time-consuming operation, mak
Externí odkaz:
http://arxiv.org/abs/2406.18568
Autor:
Rahmani, Amir Masoud, Haider, Amir, Adeli, Mohammad, Mzoughi, Olfa, Gemeay, Entesar, Mohammadi, Mokhtar, Alinejad-Rokny, Hamid, Khoshvaght, Parisa, Hosseinzadeh, Mehdi
This paper explores the efficacy of Mel Frequency Cepstral Coefficients (MFCCs) in detecting abnormal heart sounds using two classification strategies: a single classifier and an ensemble classifier approach. Heart sounds were first pre-processed to
Externí odkaz:
http://arxiv.org/abs/2406.00702