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Aug 20, 2020 · In this paper, deeper attention-based network (DAN) is proposed. With DAN method, to keep both low- and high-order features, attention average pooling layer ...
Our framework uses clustering to align comparable features and improve data organization. Multi-head attention focuses on essential characteristics, whereas the ...
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This study pioneers an improved convolutional long short-term memory with deep neural network (CLDNN) model, enriched with attention mechanisms for precise DC ...
Jun 4, 2023 · In this work, we investigate some problematic phenomena related to deep graph attention, including vulnerability to over-smoothed features and smooth ...
Sep 15, 2023 · CrabNet is a Python package for creating and training neural networks that operate on structured data, specifically matrices.
Jun 29, 2021 · Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences.
Jan 18, 2022 · TabTransformer significantly outperforms MLP and recent deep networks for tabular data while matching the performance of tree-based ensemble ...
Feb 6, 2024 · In this paper, we introduce a large-scale empirical study comparing neural networks against gradient-boosted decision trees on tabular data.
Dec 1, 2020 · This paper proposes a deep neural network that integrates the attention mechanism for group users' dynamic identification and recommendation on the Internet ...
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A transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism.