Results 91 to 100 of about 476,790 (313)
Diffusion-Based Causal Representation Learning
EntropyCausal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause–effect estimation and the identification of efficient and safe interventions.
Amir Mohammad Karimi Mamaghan +4 more
doaj +1 more source
PLoS ONE, 2013 Arousal Biased Competition theory suggests that arousal enhances competitive attentional processes, but makes no strong claims about valence effects.
Marissa A Gorlick, W Todd Maddox
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Block Orthonormal Overcomplete Dictionary Learning [PDF]
, 2013 In the field of sparse representations, the overcomplete dictionary learning problem is of crucial importance and has a growing application pool where it is used.
Dumitrescu, Bogdan, Rusu, Cristian
core +1 more source
A multimodal differential privacy framework based on fusion representation learning
, 2022 Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of ...
Chaoxin Cai +5 more
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FEBS Open Bio, EarlyView.Bioscience students were asked for their opinions on the value and teaching of skills. 204 responded that teamwork, time management and study skills are necessary to reach University, that scientific writing, research, laboratory and presentation skills are taught effectively during their studies, while other skills are gained inherently through study ...
Janella Borrell, Susan Crennell
wiley +1 more source
Entity Profiling in Knowledge Graphs
IEEE Access, 2020 Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs.
Xiang Zhang +3 more
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Guided Disentangled Representation Learning from Audio data for Transfer Learning
In the field of machine learning, disentangled representation learning seeks to map high-dimensional data into a low-dimensional space where the underlying variational factors are both disentangled and easily separable.
Haque, Kazi Nazmul
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Why human connection is the true metric of research success
FEBS Open Bio, EarlyView.Human‐centred mentorship can be shaped by mentor attributes, actions, intrinsic drive and career ambition. Drawing on reflections across Singapore and France, as well as workshop insights from FEBS‐IUBMB ENABLE 2024, this article shows that human‐centred mentorship creates the conditions for sustainable growth, well‐being and retention in research ...
Timothy Lin Yun Tan +3 more
wiley +1 more source
Network Representation Learning
大数据, 2015 Along with the constant growth of massive online social networks such as Facebook,Twitter,Weixin and Weibo,a tremendous amount of network data sets are generated.How to represent the data is an important aspect when we apply machine learning techniques ...
Weizheng Chen, Yan Zhang, Xiaoming Li
doaj
Active discriminative network representation learning [PDF]
, 2018 © 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network ...
Zhou, C +17 more
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