Results 41 to 50 of about 7,740,063 (200)
Meta-learning-based estimation of the barrier layer thickness in the tropical Indian Ocean
Environmental Research Communications, 2023 Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of the ocean’s role in climate variability on both regional and global scales.
Jifeng Qi, Tangdong Qu, Baoshu Yin
doaj +1 more source
Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning
IEEE Access, 2021 Machine Learning (ML) is a field that aims to develop efficient techniques to provide intelligent decision making solutions to complex real problems.
Robercy Alves Da Silva +3 more
doaj +1 more source
Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective
Sensors, 2021 In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing ...
Massinissa Hamidi, Aomar Osmani
doaj +1 more source
Meta Learning for Low-Resource Molecular Optimization
, 2021 The goal of molecular optimization (MO) is to discover molecules that acquire improved pharmaceutical properties over a known starting molecule. Despite many recent successes of new approaches for MO, these methods were typically developed for particular
Zheng, Shuangjia +7 more
core +1 more source
When and why does motor preparation arise in recurrent neural network models of motor control?
eLifeDuring delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement ...
Marine Schimel +2 more
doaj +1 more source
A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
Applied Sciences, 2022 Meta-learning aims to teach the machine how to learn. Embedding model-based meta-learning performs well in solving the few-shot problem. The methods use an embedding model, usually a convolutional neural network, to extract features from samples and use ...
Enjie Ding +4 more
doaj +1 more source
Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
Applied Sciences, 2022 Considering the low coverage of roadside cooperative devices at the current time, automated driving should detect all road markings relevant to driving safety, such as traffic signs that tend to be of great variety but are fewer in number.
Xinyue Ren +4 more
doaj +1 more source
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
, 2021 Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood.
Rothfuss, Jonas +3 more
core +1 more source
Frontiers in Neuroscience, 2022 Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is ...
Shuangming Yang +6 more
doaj +1 more source
CoRR, 2019 ICML 2019. The first two authors contributed equally.
Chelsea Finn +3 more
openaire +4 more sources