Soft electromechanical sensors have led to a new paradigm of electronic devices for novel
motion-based wearable applications in our daily lives. However, the vast amount of random …

Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …

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 …

Previous works for ObjectNav task attempt to learn the association (eg relation graph)
between the visual inputs and the goal during training. Such association contains the prior …

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

Autonomous robots can learn to perform visual navigation tasks from offline human
demonstrations and generalize well to online and unseen scenarios within the same …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Multi-agent trajectory forecasting has recently attracted a lot of attention due to its
widespread applications including autonomous driving. Most previous methods use RNNs …

Modeling interacting dynamical systems, such as fluid dynamics and intermolecular
interactions, is a fundamental research problem for understanding and simulating complex …