BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.
Learning From Noisy Labels With Deep Neural Networks: A Survey
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The EL-MIATTs framework offers LAF-grounded evaluation and UTTL-grounded learning strategies to handle multiple inaccurate true targets in machine learning under uncertain supervision.
citing papers explorer
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Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
BrainSimSiam applies positive-only Siamese self-supervised learning to fMRI data to produce representations that generalize across downstream tasks and outperform supervised baselines.
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LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
The EL-MIATTs framework offers LAF-grounded evaluation and UTTL-grounded learning strategies to handle multiple inaccurate true targets in machine learning under uncertain supervision.
- Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision