InfoNCE softmax misaligned with normalized embeddings per extreme value theory; WEINCE adds batch-based endpoint shortfall correction for consistent gains on five vision benchmarks.
arXiv preprint arXiv:2110.06848 , year =
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Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
citing papers explorer
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When Softmax Fails at the Top: Extreme Value Corrections for InfoNCE
InfoNCE softmax misaligned with normalized embeddings per extreme value theory; WEINCE adds batch-based endpoint shortfall correction for consistent gains on five vision benchmarks.
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Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.