The Infinite Loop by Nebius reports that AI scientists are rapidly developing across disciplines, prompting concerns over research diversity as they may lead to a scientific monoculture.

Abstract: Spiking neural networks (SNNs) have exhibited remarkable potential in neuromorphic data classification, especially in processing dynamic vision sensor (DVS) data. However, SNNs still have ...

Abstract: Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem.

How Afraid of the AI Apocalypse Should We Be? The A.I. researcher Eliezer Yudkowsky argues that we should be very afraid of artificial intelligence’s existential risks. This is an edited transcript of ...

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.

Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently ...

Tangent is a new, free, and open-source Python library for automatic differentiation. Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) ...