Results 21 to 30 of about 195,059 (253)
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Advances in Neural Information Processing Systems 36, 2023 Accepted as poster for NeurIPS ...
Muyang Li +6 more
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Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
CoRR, 2021 The theory of reinforcement learning has focused on two fundamental problems: achieving low regret, and identifying $ε$-optimal policies. While a simple reduction allows one to apply a low-regret algorithm to obtain an $ε$-optimal policy and achieve the worst-case optimal rate, it is unknown whether low-regret algorithms can obtain the instance-optimal
Andrew Wagenmaker +2 more
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Decompositional Generation Process for Instance-Dependent Partial Label Learning
CoRR, 2022 Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the ...
Congyu Qiao, Ning Xu 0009, Xin Geng 0001
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Instance-Dependent Noisy Label Learning via Graphical Modelling
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymmetric and instance-dependent noise (IDN), with IDN being the only type that depends on image information.
Arpit Garg +4 more
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Model independent feature attributions: Shapley values that uncover non-linear dependencies [PDF]
PeerJ Computer Science, 2021 Shapley values have become increasingly popular in the machine learning literature, thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain notions of ‘fairness’. The flexibility arises from the myriad potential forms
Daniel Vidali Fryer +2 more
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Prospective Coding by Spiking Neurons. [PDF]
PLoS Computational Biology, 2016 Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting.
Johanni Brea +3 more
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Derivation of a novel efficient supervised learning algorithm from cortical-subcortical loops
Frontiers in Computational Neuroscience, 2012 Although brain circuits presumably carry out useful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications.
Ashok eChandrashekar, Richard eGranger
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Agronomy, 2022 Phenotype analysis of leafy green vegetables in planting environment is the key technology of precision agriculture. In this paper, deep convolutional neural network is employed to conduct instance segmentation of leafy greens by weakly supervised ...
Zhuang Qiang, Jingmin Shi, Fanhuai Shi
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Rethinking the Value of Labels for Instance-Dependent Label Noise Learning
CoRR, 2023 Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms. Due to the non-identifiability of the instance-dependent noise transition matrix, most existing algorithms address the problem by assuming the noisy label generation process to be independent of the instance features ...
Hanwen Deng +2 more
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Instance-Dependent Multilabel Noise Generation for Multilabel Remote Sensing Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingMultilabel remote sensing image classification is a fundamental task that classifies multiple objects and land covers within an image. However, training deep learning models for this task requires a considerable cost of labeling. While several efforts to
Youngwook Kim +3 more
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