Results 31 to 40 of about 259,517 (255)
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
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
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Deep Neural Network for Emotion Recognition Based on Meta-Transfer Learning
IEEE Access, 2022 In recent years, many EEG-based emotion recognition methods have been proposed, which can achieve good performance on single-subject data. However, when the models are applied to cross-subject scenarios, due to the existence of subject differences, these
Hengyao Tang +2 more
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ROA: A Rapid Learning Scheme for In-Situ Memristor Networks
Frontiers in Artificial Intelligence, 2021 Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and ...
Wenli Zhang +4 more
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Online Learning of a Memory for Learning Rates
, 2018 The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks.
Kappler, Daniel +2 more
core +1 more source
In search of the neural circuits of intrinsic motivation
Frontiers in Neuroscience, 2007 Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories.
Frederic Kaplan, Pierre-Yves Oudeyer
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A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
, 2013 The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics,
Pandey, Gaurav, Whalen, Sean
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Multi-Stage Meta-Learning for Few-Shot with Lie Group Network Constraint
Entropy, 2020 Deep learning has achieved many successes in different fields but can sometimes encounter an overfitting problem when there are insufficient amounts of labeled samples.
Fang Dong, Li Liu, Fanzhang Li
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Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion
Entropy, 2022 The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks.
Shuangming Yang +2 more
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Improving Domain-Generalized Few-Shot Text Classification with Multi-Level Distributional Signatures
Applied Sciences, 2023 Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains.
Xuyang Wang +8 more
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