Results 101 to 110 of about 6,929,542 (320)

Fairness-Aware Hyperparameter Optimization [PDF]

, 2020
In recent years, increased usage of machine learning algorithms has been accompanied by several reports of machine bias in areas from recidivism assessment, to job-applicant screening tools, and estimating mortgage default risk.
André Miguel Ferreira da Cruz
core   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

Advanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose, Sanxchep Sharma, Veit Königer, Vincent Nebel, Jonas Oehm, Christian Weisenberger, Laura Bies, Volker Knoblauch   +7 more
wiley   +1 more source

Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview

Hyperparameter optimization constitutes a large part of typical modern machine learning (ML) workflows. This arises from the fact that ML methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly
Karl, Florian M., Bischl, Bernd, Pielok, Tobias, Schneider, Lennart, Branke, Juergen, Binder, Martin, Richter, Jakob, Pfisterer, Florian, Moosbauer, Julia, Lang, Michel, Garrido Merchán, Eduardo C., Coors, Stefan, Thomas, Janek   +12 more
core   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source

Hyperparameter optimization for randomized algorithms: a case study on random features [PDF]

Statistics and computing
Randomized algorithms exploit stochasticity to reduce computational complexity. One important example is random feature regression (RFR) that accelerates Gaussian process regression (GPR). RFR approximates an unknown function with a random neural network
Oliver R. A. Dunbar, N. Nelsen, Maya Mutic   +2 more
semanticscholar   +1 more source

Hyperparameter Optimization for Effort Estimation

CoRR, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies   +5 more
openaire   +3 more sources

Multimode Oxide‐Based Optoelectronic Memtransistor for In‐Sensor Vision Processing

Advanced Functional Materials, EarlyView.
A multimode optoelectronic memtransistor (OEMT) is demonstrated for vision explainable artificial intelligence (VXAI) hardware. By integrating optical sensing, electrical masking, and non‐volatile memory, the device enables key operations required for generating saliency information.
Min Gu Lee, Geunyoung Kim, Hwa Young Kim, Do Hoon Kim, Hanchan Song, Huisu Noh, Jungwan Noh, Seungwoo Lee, Sang‐Hee Ko Park, Himchan Cho, Kyung Min Kim   +10 more
wiley   +1 more source

An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

Geomatics, Natural Hazards & Risk
This study proposed an interpretable model that combines Random Forest (RF), Optuna hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to achieve optimal landslide susceptibility evaluation and provide explanations in the northwest ...
Xin Xiao, Yi Zou, Jiangcheng Huang, Xuan Luo, Lu-yi Yang, Meng Li, Pengwu Yang, Xuan Ji, Yun-Gang Li   +8 more
semanticscholar   +1 more source

No-Regret Bayesian Optimization with Unknown Hyperparameters

J. Mach. Learn. Res., 2019
ISSN:1532 ...
Felix Berkenkamp, Angela P. Schoellig, Andreas Krause 0001   +2 more
openaire   +5 more sources