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University Research

Scientists from ETH Zurich, EPFL, and Microsoft have presented QuaRot, a new machine learning technique that facilitates 4-bit inference of Latent Linear Models (LLMs) by eliminating unconventional features.

Large language models (LLMs) have substantially impacted various applications across sectors by offering excellent natural language processing capabilities. They help generate, interpret, and understand the human language, opening routes for new technological advancements. However, LLMs demand considerable computational, memory, and energy resources, particularly during the inference phase, which restricts operational efficiency and their deployment. The extensive…

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FeatUp: An Advanced Machine Learning Algorithm that Enhances the Resolution of Deep Neural Networks for Superior Performance in Computer Vision Activities

The capabilities of computer vision studies have been vastly expanded due to deep features, which can unlock image semantics and facilitate diverse tasks, even using minimal data. Techniques to extract features from a range of data types – for example, images, text, and audio – have been developed and underpin a number of applications in…

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