In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …

Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have
achieved a substantial scientific leap in the past decade. However, even with the advent of …

Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …

Dataset distillation compresses large datasets into smaller synthetic coresets which retain
performance with the aim of reducing the storage and computational burden of processing …

Deterministic neural networks (NNs) are increasingly being deployed in safety critical
domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

We propose a deep neural network (DNN) as a fast surrogate model for local stress
calculations in inhomogeneous non-linear materials. We show that the DNN predicts the …

We introduce a new class of time-continuous recurrent neural network models. Instead of
declaring a learning system's dynamics by implicit nonlinearities, we construct networks of …

A proper parametrization of state transition matrices of linear state-space models (SSMs)
followed by standard nonlinearities enables them to efficiently learn representations from …

Simulation has the potential to transform the development of robust algorithms for mobile
agents deployed in safety-critical scenarios. However, the poor photorealism and lack of …