GCRL and MISL are unified as control maximization, with three inequivalent GCRL formulations each matched to a MISL objective via bounds on goal-sensitivity.
Masked autoencoders are scalable vision learners
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PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.
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Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
GCRL and MISL are unified as control maximization, with three inequivalent GCRL formulations each matched to a MISL objective via bounds on goal-sensitivity.
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PoDAR: Power-Disentangled Audio Representation for Generative Modeling
PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.