Results 221 to 230 of about 195,059 (253)
How does stochasticity in learning impact the accumulation of knowledge and the evolution of learning? [PDF]
Evol Hum SciMaisonneuve L, Lehmann L.
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Open questions in elucidating neural mechanisms underlying sensory-guided motor control. [PDF]
Nat Communeuropepmc +1 more source
Instance-dependent cost-sensitive parametric learning
NeurocomputingThis research has been financed by the Grant PID2020-113578RB-I00 and PID2023-147127OB-I00 ”ERDF/EU”, funded by the sponsor MCIN/AEI/10.13039/501100011033/, Spain. It has also been supported by the Xunta de Galicia, Spain (Grupos de Referencia Competitiva ED431C-2024/14) and by CITIC as a center accredited for excellence within the Galician University ...
Gerda Claeskens, Jorge C-Rella
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Instance-Dependent Inaccurate Label Distribution Learning
IEEE Transactions on Neural Networks and Learning SystemsLabel distribution learning (LDL) is a novel learning paradigm that assigns each instance with a label distribution. Although many specialized LDL algorithms have been proposed, few of them have noticed that the obtained label distributions are generally inaccurate with noise due to the difficulty of annotation.
Xin Geng, Yuheng Jia, Jing Wang
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Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Partial label learning (PLL) learns from a typical weak supervision, where each training instance is labeled with a set of ambiguous candidate labels (CLs) instead of its exact ground-truth label. Most existing PLL works directly eliminate, rather than exploiting the label ambiguity, since they explicitly or implicitly assume that incorrect CLs are ...
Shiyu Xia +3 more
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Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022This paper studies instance-dependent Positive and Unlabeled (PU) classification, where whether a positive example will be labeled (indicated by s) is not only related to the class label y, but also depends on the observation x. Therefore, the labeling probability on positive examples is not uniform as previous works assumed, but is biased to some ...
Chen Gong 0002 +6 more
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Neural Networks
Instance-dependent noise (IDN) widely exists in real-world datasets, seriously hindering the effective application of deep neural networks. In contrast to class-dependent noise, IDN is influenced not solely by the class but also by the intrinsic features of the instance.
Shikui Wei, Ying Qin
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Instance-dependent noise (IDN) widely exists in real-world datasets, seriously hindering the effective application of deep neural networks. In contrast to class-dependent noise, IDN is influenced not solely by the class but also by the intrinsic features of the instance.
Shikui Wei, Ying Qin
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Variational Label Enhancement for Instance-Dependent Partial Label Learning
IEEE Transactions on Pattern Analysis and Machine IntelligencePartial label learning (PLL) is a form of weakly supervised learning, where each training example is linked to a set of candidate labels, among which only one label is correct. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, in practice, this assumption may not
Xin Geng, Ning Xu, Min-Ling Zhang
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Instance-Dependent ℓ∞-Bounds for Policy Evaluation in Tabular Reinforcement Learning
IEEE Transactions on Information Theory, 2021Markov reward processes (MRPs) are used to model stochastic phenomena arising in operations research, control engineering, robotics, and artificial intelligence, as well as communication and transportation networks. In many of these cases, such as in the policy evaluation problem encountered in reinforcement learning, the goal is to estimate the long ...
Ashwin Pananjady, Martin J. Wainwright
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Mitigating Heterogeneous Instance-Dependent Label Noise in Federated Learning
Lecture Notes in Computer Scienceexaly +2 more sources