Results 171 to 180 of about 2,019,981 (302)

Bayesian Multicriterial Optimization [PDF]

Tato práce představím základní prvky Bayesovské optimalizace, jako jsou Gaussovské procesy, akviziční funkce a hyperparametry. Dále poskytnu názorné příklady optimalizace hyperparametrů, vlivu hyperparametrů na Gaussovský proces a Bayesovské optimalizace.
Stanislav Žák
core  

Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

Advanced Science, EarlyView.
BOND‐PEP enables controllable, sequence‐first peptide binder design by grounding generation in binding evidence retrieved for each target. It uses topology‐conditioned message passing to integrate relevant peptide examples with the target protein sequence, forming a residue‐level representation that guides the generation of diverse, target‐specific ...
Wenze Ding
wiley   +1 more source

Fusing Direct and Indirect Measurements Through Multi‐Fidelity Learning For Accelerated Electrocaloric Materials Discovery

Advanced Science, EarlyView.
A multi‐fidelity framework integrates sparse direct and abundant indirect electrocaloric measurements. Multi‐objective active learning accelerates BaTiO3‐based electrocaloric materials discovery at –70∘C$^{\circ }{\rm C}$. A diffuse transition enables an electrocaloric strength of 0.06×$\times$10−6 Km/V at –70℃ with an operational temperature span of ...
Bo Wang, Pengfei Dang, Yuan Tian, Zhengwang He, Wei Gu, Lixue Zhang, Turab Lookman, Yumei Zhou, Dezhen Xue   +8 more
wiley   +1 more source

Retraction notice to "Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques" [Heliyon 10 (2024) e26192]. [PDF]

Heliyon
Li L, Yang J, Por LY, Khan MS, Hamdaoui R, Hussain L, Iqbal Z, Rotaru IM, Dobrotă D, Aldrdery M, Omar A.   +10 more
europepmc   +1 more source

Sustainable Hyperparameter Optimization

https://www.ieeesmc.org/cai-2026/tutorial-7-sustainable-hyperparameter-optimization/ https://github.com/ai-for-decision-making-tue ...
AMINI, Sasan, Bliek, Laurens
openaire   +2 more sources

A characteristic of 66 selected studies for the unbalanced outcome, a technique used for an unbalanced outcome, data preprocessing, hyperparameters optimization, and performance measurement used.

A characteristic of 66 selected studies for the unbalanced outcome, a technique used for an unbalanced outcome, data preprocessing, hyperparameters optimization, and performance measurement used.
Qing Wu (277441), Jingyuan Dai (11565754), Jongyun Jung (13014288), Bowen Liu (2009599)   +3 more
core   +1 more source

A Site‐Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design

Advanced Science, EarlyView.
MolDBG is a site‐aware, sequence‐only framework that unifies drug‐target affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation for structured proteins. Guided by multi‐task binding‐site supervision, it aligns interaction‐critical residues before learning drug‐target representations and simultaneously infers ...
Gang Luo, Qianqian Zhang, Chenhao Wang, Zhiheng Yi, Alex Jinpeng Wang, Jing Tang, Min Li   +6 more
wiley   +1 more source

Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization. [PDF]

Sci Rep
Sawah MS, Elmannai H, El-Bary AA, Lotfy K, Sheta OE.   +4 more
europepmc   +1 more source

Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions

Advanced Science, EarlyView.
A unified time‐series forecasting framework learns transferable battery capacity‐degradation patterns from 20 heterogeneous datasets spanning chemistries, formats, temperatures, and cycling conditions. A single model delivers competitive predictions on both known and previously unseen datasets, while physics‐guided representation learning improves ...
Joey Chan, Huan Wang, Zhen Chen, Haoyu Pan, Wei Wu, Zirong Wang, Ershun Pan, Min Xie, Lifeng Xi   +8 more
wiley   +1 more source

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification. [PDF]

J Imaging
Aguerchi K, Jabrane Y, Habba M, El Hassani AH.   +3 more
europepmc   +1 more source