Accurate state of charge (SOC) constitutes the basis for reliable operations of lithium-ion
batteries. The deep learning technique, a game changer in many fields, has recently …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

A central goal of sequence modeling is designing a single principled model that can
address sequence data across a range of modalities and tasks, particularly on long-range …

Graph convolutional networks (GCNs) have been widely used and achieved remarkable
results in skeleton-based action recognition. In GCNs, graph topology dominates feature …

Models using structured state space sequence (S4) layers have achieved state-of-the-art
performance on long-range sequence modeling tasks. An S4 layer combines linear state …

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …

Human action recognition aims at classifying the category of human action from a segment
of a video. Recently, people have dived into designing GCN-based models to extract …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Spatial-temporal graphs have been widely used by skeleton-based action recognition
algorithms to model human action dynamics. To capture robust movement patterns from …