Feb 10, 2023 · In this paper, we propose a novel attention-based method that can skip the useless features and highlight the task-specific information.
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Abstract. Based on massive data, deep neural networks have been proven to have a powerful learning capability of non-linear relationships.
To address this issue, in this paper, we propose a novel attention-based method that can skip the useless features and highlight the task-specific information.
A Task-Aware Attention-Based Method for Improved Meta-Learning ... Authors: Yue Zhang; Xinxing Yang; Feng Zhu; Yalin Zhang; Meng Li; Qitao Shi; Longfei Li; Jun ...
A novel approach of meta-learning model based-on attention mechanisms, ensemble learning and metric learning is established in this study.
Missing: Aware | Show results with:Aware
May 23, 2024 · We introduce a novel architecture, AttFPGNN-MAML, for few-shot molecular property prediction. The proposed approach incorporates a hybrid feature ...
Jul 13, 2020 · A task hardness aware module is designed and integrated into the training procedure to estimate the hardness of a task based on the distinguishability of its ...
Missing: Improved | Show results with:Improved
This paper investigates the use of nonparametric kernel-regression to obtain a task-similarity aware meta-learning algorithm.
We propose a novel task representation called model-aware task embedding (MATE) that incorporates not only the data distributions of different tasks, but also ...
Missing: Method | Show results with:Method
The proposed method improves upon MAML by speeding up the adaptation and giving stronger few-shot learn- ing ability. Experimental results on the few-shot.