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.

A complete walkthrough of implementing the original Attention Is All You Need encoder-decoder Transformer—no torch. nn.Transformer, no shortcuts. The 2017 paper "Attention Is All You Need" by Vaswani ...

Credit: VentureBeat made with Flux.2 Pro on fal.ai For the last two years, the prevailing logic in generative AI has been one of brute force: if you want better reasoning, you need a bigger model.

Implementation of Hindsight Differentiable Policy Optimization, as described in the paper Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization. We argue that ...

Recurrent neural networks (RNNs) have been proved very successful at modeling sequential data such as language or motions. However, these successes rely on the use of the backpropagation through time ...

A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities ...