Richard Xu — accepted AI-conference papers

2022

Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity

Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravinthan Samuel +1

ICLR 2022poster

The availability of both anatomical connectivity and brain-wide neural activity measurements in C. elegans make the worm a promising system for learning detailed, mechanistic models of an entire nervous system in a data-driven way. However, one faces several challenges when constructing such a model…

Cited by 13 Source ↗PDF ↗Scholar ↗

2022

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective

Wei Huang, Yayong Li, weitao Du, Richard Xu, Jie Yin, Ling Chen +1

ICLR 2022poster

Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The t…

Cited by 33 Source ↗PDF ↗Scholar ↗