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Causality-driven feature representation for connectivity prediction [PDF]
Frontiers in Artificial IntelligenceCausal reasoning is essential for understanding relationships and guiding decision-making in different applications, as it allows for the identification of cause-and-effect relationships between variables. By uncovering the underlying process that drives
Bruno Souza +6 more
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Feature representation for explainable CRISPR off-target prediction and base editing efficiency [PDF]
Frontiers in BioinformaticsIntroductionThe interaction between guide RNAs (gRNAs) and target DNA sequences is a critical factor in the effectiveness of CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) gene editing.
Faiza Hasin +8 more
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Auto-Encoding Generative Adversarial Networks towards Mode Collapse Reduction and Feature Representation Enhancement [PDF]
Entropy, 2023 Generative Adversarial Nets (GANs) are a kind of transformative deep learning framework that has been frequently applied to a large variety of applications related to the processing of images, video, speech, and text.
Yang Zou, Yuxuan Wang, Xiaoxiang Lu
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MSDSANet: Multimodal Emotion Recognition Based on Multi-Stream Network and Dual-Scale Attention Network Feature Representation [PDF]
SensorsAiming at the shortcomings of EEG emotion recognition models in feature representation granularity and spatiotemporal dependence modeling, a multimodal emotion recognition model integrating multi-scale feature representation and attention mechanism is ...
Weitong Sun +4 more
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UNSUPERVISED TRANSUDATIVE TL FEATURE LEARNING FOR IMAGE FEATURE EXTRACTION AND REPRESENTATION [PDF]
ICTACT Journal on Image and Video Processing, 2023 In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in ...
Logeshwari Dhavamani +3 more
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IEEE Access, 2021 Machine learning has been widely applied in the fields of biomedicine, computational biology, bioinformatics, image processing, and so on. The performance of machine learning methods mainly relies on feature representation that is the mapping from ...
Feifei Cui +5 more
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Successor Feature Representations
Trans. Mach. Learn. Res., 2021 published in Transactions on Machine Learning Research (05/2023), source code: https://gitlab.inria.fr/robotlearn/sfr_learning, [v2] added experiments with learned features, [v3] renamed paper and changed scope, [v4] published ...
Reinke, Chris, Alameda-Pineda, Xavier
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A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers
IEEE Access, 2021 During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users.
David Martin-Gutierrez +4 more
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A Unified Feature Representation for Lexical Connotations [PDF]
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021 EACL ...
Allaway, Emily, McKeown, Kathleen
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Molecules, 2022 The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity.
Yue-Hua Feng, Shao-Wu Zhang
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