Medical imaging is a cornerstone of modern clinical practice, providing indispensable insights for diagnosis, treatment planning, and disease monitoring. A wide array of medical image analysis tools, ...
[2026.03.09] We have updated our technical report with more experimental results. By integrating contrastive, self-supervised, and reconstruction learning, we have trained numerous visual tokenizers ...
Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed ...
Abstract: Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely ...
Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE ...
Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which ...
The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships ...
2021 NeurIPS Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection 2021 NeurIPS Exploring the Limits of Out-of-Distribution Detection 2021 NeurIPS Locally Most ...
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