Results 31 to 40 of about 8,974 (165)
Temporal Variation and Vertical Structure of the Marine Atmospheric Mixed Layer over the East China Sea from Mie-Scattering Lidar Data [PDF]
, 2011 The marine atmospheric mixed layer (MAML) has an important influence on the diffusion of air pollutants over the East China Sea. We analyzed seasonal and diurnal variations and the vertical structure of the MAML by using continuous Mie-scattering lidar ...
Kuribayashi Masatoshi +3 more
core +1 more source
Zero-Shot Cross-Lingual Transfer with Meta Learning
, 2020 Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance.
Augenstein, Isabelle +3 more
core +1 more source
Finding Meta Winning Ticket to Train Your MAML
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 The lottery ticket hypothesis (LTH) states that a randomly initialized dense network contains sub-networks that can be trained in isolation to the performance of the dense network. In this paper, to achieve rapid learning with less computational cost, we explore LTH in the context of meta learning.
GAO, Dawei +5 more
openaire +2 more sources
Magnetosome Gene Duplication as an Important Driver in the Evolution of Magnetotaxis in the Alphaproteobacteria [PDF]
, 2019 The evolution of microbial magnetoreception (or magnetotaxis) is of great interest in the fields of microbiology, evolutionary biology, biophysics, geomicrobiology, and geochemistry.
Bazylinski, Dennis A. +9 more
core +4 more sources
Meta-Learning with MAML on Trees
, 2021 In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks with a hierarchical structure.
Garcia, Jezabel R. +7 more
openaire +2 more sources
Plant Methods, 2021 Background Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend.
Jing Nie +4 more
doaj +1 more source
MAML and ANIL Provably Learn Representations
, 2022 Recent empirical evidence has driven conventional wisdom to believe that gradient-based meta-learning (GBML) methods perform well at few-shot learning because they learn an expressive data representation that is shared across tasks. However, the mechanics of GBML have remained largely mysterious from a theoretical perspective.
Collins, Liam +3 more
openaire +2 more sources
Graph Few-shot Learning via Knowledge Transfer
, 2020 Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating ...
Chawla, Nitesh V. +7 more
core +1 more source
Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size
Machines, 2022 Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface.
Yuefan Zhu +4 more
doaj +1 more source
Optimizing Seizure Prediction From Reduced Scalp EEG Channels Based on Spectral Features and MAML
IEEE Access, 2021 Epilepsy is a severe neurological disease with high prevalence and morbidity worldwide. The unpredictability of seizures prevents physicians from tailoring drugs and therapies.
Anibal Romney, Vidya Manian
doaj +1 more source