The study presents a method of dynamically selecting layers in deep transformer networks to reduce the number of trainable parameters. This is achieved by employing Reinforcement Learning to decide whether…
Continue readingThe article ‘Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification’ discusses a common issue in multi-label classification (MLC) tasks, the ‘Sigmoid Bottleneck’. This refers to the occurrence of ‘unargmaxable’…
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