S-JEPA uses soft GMM posteriors in a JEPA framework for self-supervised speech learning, achieving lowest WER below 90M parameters without offline re-clustering.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.
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S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning
S-JEPA uses soft GMM posteriors in a JEPA framework for self-supervised speech learning, achieving lowest WER below 90M parameters without offline re-clustering.
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CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.