The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the
continuous limit of the classical momentum accelerated gradient descent, to improve neural …

Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …

Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental
problem in many areas. Pure Machine Learning (ML) approaches often fail in the small …

The combination of ordinary differential equations and neural networks, ie, neural ordinary
differential equations (Neural ODE), has been widely studied from various angles. However …

Meta-learning is widely used in few-shot classification and function regression due to its
ability to quickly adapt to unseen tasks. However, it has not yet been well explored on …

Neural SDEs combine many of the best qualities of both RNNs and SDEs, and as such are a
natural choice for modelling many types of temporal dynamics. They offer memory efficiency …

The standard approaches to neural network implementation yield powerful function
approximation capabilities but are limited in their abilities to learn meta representations and …

Neural controlled differential equations (Neural CDEs) are a continuous-time extension of
recurrent neural networks (RNNs), achieving state-of-the-art (SOTA) performance at …

Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point
processes enhance the expressivity of point process models with deep neural networks …