Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

How can we design protein sequences folding into the desired structures effectively and
efficiently? AI methods for structure-based protein design have attracted increasing attention …

Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …

Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

Efficiently modeling spatio-temporal (ST) physical processes and observations presents a
challenging problem for the deep learning community. Many recent studies have …

Ocean eddies affect large‐scale circulation and induce a kinetic energy cascade through
their non‐linear interactions. However, since global observations of eddy dynamics come …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of
spatio-temporal prediction networks. Despite significant progress in this field, there remains …

In recent years traditional numerical methods for accurate weather prediction have been
increasingly challenged by deep learning methods. Numerous historical datasets used for …