Results 91 to 100 of about 571,567 (314)

Benchmark Health Index: A Systematic Framework for Benchmarking the Benchmarks of LLMs

CoRR
Large Language Models (LLMs) are advancing rapidly, yet the benchmarks used to measure this progress are becoming increasingly unreliable. Score inflation and selective reporting have eroded the authority of standard benchmarks, leaving the community uncertain about which evaluation results remain trustworthy.
Longyuan Zhu, Hairan Hua, Linlin Miao, Bing Zhao   +3 more
openaire   +2 more sources

A Benchmark Approach to Investing and Pricing [PDF]

This paper introduces a general market modeling framework, the benchmark approach, which assumes the existence of the numeraire portfolio. This is the strictly positive portfolio that when used as benchmark makes all benchmarked nonnegative portfolios ...
Eckhard Platen
core  

Minimal Important Change and Minimal Clinically Important Difference in Pain and Function With Exercise in Hip Osteoarthritis

Arthritis Care &Research, EarlyView.
Objective The objective of this study was to estimate the minimal important change (MIC) and minimal clinically important difference (MCID) for pain and physical function in individuals with hip osteoarthritis (OA) following a physiotherapist‐guided exercise intervention.
Yareni Guerrero, Fiona Dobson, Venkatesha Venkatesha, Kim Allison, Gabrielle Knox, Libby Spiers, Travis Haber, David J Hunter, Michelle Hall   +8 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Exploring the feasibility of integrating ultra‐high field magnetic resonance imaging neuroimaging with multimodal artificial intelligence for clinical diagnostics

iRADIOLOGY
Background The integration of 7 Tesla (7T) magnetic resonance imaging (MRI) with advanced multimodal artificial intelligence (AI) models represents a promising frontier in neuroimaging.
Yifan Yuan, Kaitao Chen, Youjia Zhu, Yang Yu, Mintao Hu, Ying‐Hua Chu, Yi‐Cheng Hsu, Jie Hu, Qi Yue, Mianxin Liu   +9 more
doaj   +1 more source

A Hitchhiker Guide to Structural Variant Calling: A Comprehensive Benchmark Through Different Sequencing Technologies

Biomedicines
Background: Structural variants (SVs) play a significant role in gene function and are implicated in numerous human diseases. With advances in sequencing technologies, identifying SVs through whole-genome sequencing (WGS) has become a key area of ...
Giuseppe Giovanni Nardone, Valentina Andrioletti, Aurora Santin, Anna Morgan, Beatrice Spedicati, Maria Pina Concas, Paolo Gasparini, Giorgia Girotto, Ivan Limongelli   +8 more
doaj   +1 more source

SSoelvsten/bdd-benchmark: TACAS 2022

, 2021
The BDD Benchmark repository at the time of submitting our paper on Adiar 1.0.1 to TACAS 2022. Quite a lot of experiments were left out of the TACAS paper due to space constraints, but all of them are described in the arXiv paper.
Steffan Sølvsten
core   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

DERI1000: A New Benchmark for Dataset Explainability Readiness

AI
Deep learning models are increasingly evaluated not only for predictive accuracy but also for their robustness, interpretability, and data quality dependencies.
Andrej Pisarcik, Robert Hudec, Roberta Hlavata   +2 more
doaj   +1 more source

Benchmark Data Repositories for Better Benchmarking

Advances in Neural Information Processing Systems 37
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- and levies criticisms at -- data and benchmarking practices in machine learning, comparatively less attention has been paid to the data repositories where these datasets are ...
Rachel Longjohn, Markelle Kelly, Sameer Singh 0001, Padhraic Smyth   +3 more
openaire   +3 more sources