Results 71 to 80 of about 9,064 (165)
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD
, 2021 We propose a new computationally-efficient first-order algorithm for Model-Agnostic Meta-Learning (MAML). The key enabling technique is to interpret MAML as a bilevel optimization (BLO) problem and leverage the sign-based SGD(signSGD) as a lower-level optimizer of BLO.
Fan, Chen, Ram, Parikshit, Liu, Sijia
openaire +2 more sources
IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.Integrated IDS pipeline for anomaly detection in vehicular ad hoc networks (VANETs), from domain construction to adapter tuning, with decision nodes and diagnostic feedback. ABSTRACT Vehicular ad hoc networks (VANETs) face critical security threats, including false position reports, replayed messages and denial of service (DoS) attacks, which can ...
Abuzar Khan +4 more
wiley +1 more source
, 2019 The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning.
Antoniou, Antreas +2 more
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La-MAML: Look-ahead Meta Learning for Continual Learning
, 2020 The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for reducing interference between old and new tasks, the current training procedures tend to be either slow or offline, and sensitive to many hyper ...
Gupta, Gunshi +2 more
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American Journal of Hematology, Volume 101, Issue 3, Page 577-580, March 2026.
Marina Konopleva +8 more
wiley +1 more source
HIF1α Attenuated the Lung Ischemia–Reperfusion Injury by Activating the miR‐485/Notch1 Signalling
Journal of Cellular and Molecular Medicine, Volume 30, Issue 1, January 2026.ABSTRACT Lung ischemia–reperfusion (I/R) injury is a common complication following lung transplantation and cardiac surgery. This study aimed to investigate the role and underlying mechanisms involving Neurogenic locus notch homologue protein 1 (Notch1) signalling and microRNA‐485 (miR‐485) of hypoxia‐inducible factor 1‐alpha (HIF1α) in protecting ...
Shaohua Dai +6 more
wiley +1 more source
How Does the Task Landscape Affect MAML Performance?
, 2020 Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over
Collins, Liam +2 more
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Fixed-MAML for Few-shot Classification in Multilingual Speech Emotion Recognition
, 2022 In this paper, we analyze the feasibility of applying few-shot learning to speech emotion recognition task (SER). The current speech emotion recognition models work exceptionally well but fail when then input is multilingual. Moreover, when training such models, the models' performance is suitable only when the training corpus is vast.
Naman, Anugunj +2 more
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Shared Minds: The Cognitive Parallels Between Humans and Artificial Intelligence
Human Behavior and Emerging Technologies, Volume 2026, Issue 1, 2026.This narrative review integrates evidence from cognitive science and AI research to challenge commonly accepted dichotomies between human and artificial cognition, such as the assumed divide between genuine human understanding and mere machine pattern matching. Instead, we propose a view that recognises similarities in their cognitive architectures and
Sébastien Tremblay +4 more
wiley +1 more source
ES-MAML: Simple Hessian-Free Meta Learning
, 2019 We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to
Song, Xingyou +5 more
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