Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Scripps Research scientists used a graphical neural network-based structure building tool, ModelAngelo, to discover monoclonal antibodies (bottom) from polyclonal antibody responses produced after ...

Abstract: The principal innovative contribution of this study resides in the introduction of a category of fractional delayed large-scale neural networks characterized by intricate topological ...

The applications of neural network models, shallow or deep, to information retrieval (IR) tasks falls under the purview of neural IR. Over the years, machine learning methods-including neural networks ...

CPUs and GPUs are old news. These days, the cutting edge is all about NPUs, and hardware manufacturers are talking up NPU performance. The NPU is a computer component designed to accelerate AI tasks ...

Abstract: We investigate the efficiency of deep neural networks for approximating scoring functions in diffusion-based generative modeling. While existing approximation theories leverage the ...

An important step before clinical intervention selection is the diagnosis of the condition of a patient. Diagnostic tests are commonly used to confirm or exclude a target condition (e.g. a disease).

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

Theoretical physicist John Hopfield is one of the winners of the 2024 Nobel Prize in Physics. Theoretical physicist John Hopfield is one of the winners of the 2024 Nobel Prize in Physics. In 1982, in ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.