Results 231 to 240 of about 109,092 (251)

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel, Pierre Baldi, Daniel L. Gillen   +2 more
openaire   +1 more source

Hyperparameter Optimization Machines

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +1 more source

Learning hyperparameter optimization initializations

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +1 more source

Model-based hyperparameter optimization

2023
L’objectif principal de ce travail est de proposer une méthodologie de découverte des hyperparamètres. Les hyperparamètres aident les systèmes à converger lorsqu’ils sont bien réglés et fabriqués à la main. Cependant, à cette fin, des hyperparamètres mal choisis laissent les praticiens dans l’incertitude, entre soucis de mise en oeuvre ou mauvais choix
openaire   +1 more source

Results for "Optimizing hyperparameters"

2020
Output from optimizing hyperparmeters, find scripts on https://github.com/asreview/paper-optimizing ...
van de Schoot, Rens, de Bruin, Jonathan, Ferdinands, Gerbrich   +2 more
openaire   +1 more source