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3d
Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding
Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Medical Sensors and Imaging will publish innovative research at the intersection of engineering, biomedical and computer sciences ...
A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside ...
Quantum computer research is advancing at a rapid pace. Today's devices, however, still have significant limitations: For example, the length of a quantum computation is severely limited—that is, the ...
Researchers from Kyushu University have developed an innovative computational method, called ddHodge, that can reconstruct the complex dynamics of how cells decide their fate. As reported in Nature ...
Researchers from Chinese Academy of Sciences propose a hierarchical Physics-Informed AI framework that balances model fidelity with speed, paving the way for reliable Digital Twins Researchers from ...
A multicolored 3D model of an enzyme binding double-stranded DNA against a light purple background Computational enzyme design could allow for reactions not seen in nature. Credit: Ian Haydon ...
Researchers from Turku Bioscience Centre at the University of Turku, Finland, have developed a new computational method to interpret complex single-cell data. The method helps researchers identify and ...
A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.
A study by the Barcelona Supercomputing Center analysed molecular data from more than 4,000 patients and 45 diseases using a new computational method designed to scientifically explain the clinical ...
Conformational entropy associated with thermal vibrations plays fundamental roles in protein function, from ligand binding and catalysis to allosteric regulation. Cooper and Dryden first proposed ...
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