Results 61 to 70 of about 271,594 (275)
Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement
IEEE Access, 2021 Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models
Weili Zhou, Mingliang Lu, Ruijie Ji
doaj +1 more source
A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller
International Journal of Adaptive Control and Signal Processing, EarlyView.Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg +5 more
wiley +1 more source
Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection
SensorsWith the increasing demand for time-series analysis, driven by the proliferation of IoT devices and real-time data-driven systems, detecting change points in time series has become critical for accurate short-term prediction.
Jessica Fernandes Lopes +2 more
doaj +1 more source
Leida xuebao, 2022 The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage ...
Yixuan LYU +7 more
doaj +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 +2 more
wiley +1 more source
A meta-learning approach to improving transferability for freeway traffic crash risk prediction
Digital Transportation and SafetyCrash risk prediction plays a vital role in preventing freeway traffic accidents. Due to the limited availability of crash data in some freeway sections, model transferability of crash risk prediction has become an essential topic in traffic safety ...
Chenlei Liao, Xiqun (Michael) Chen
doaj +1 more source
Meta-Learning for Phonemic Annotation of Corpora [PDF]
, 2000 We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating
Daelemans, W. +5 more
core +6 more sources
Exploring the similarity of medical imaging classification problems
, 2017 Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification ...
Bozorg, Behdad Dasht +4 more
core +1 more source
Meta-learning for Safety Management
Chemical Engineering Transactions, 2020 The experience gathered from normal industrial operations allows us to associate its degrading conditions with the potential for an accident. Such association is the basis for the definition of the system risk and appropriate safety measures. If a skilled operator observes further degrading conditions, his/her mind quickly learns from this new ...
Paltrinieri N. +4 more
openaire +5 more sources
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source