342 citations · 1.8k across the 90 of their papers we have counts for
8 papers · 1 filter
An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion
Peng Jin, Yinan Feng, Shihang Feng +5
This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the p…
Federated Learning for Inference at Anytime and Anywhere
Zicheng Liu, Da Li, Javier Fernandez-Marques +6
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication…
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Lirong Wu, Yufei Huang, Haitao Lin +3
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning. With hundreds of self-supervised pretext tasks proposed over the past…
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Lirong Wu, Jun Xia, Haitao Lin +4
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
Harnessing Hard Mixed Samples with Decoupled Regularizer
Zicheng Liu, Siyuan Li, Ge Wang +3
Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup met…
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop
Peng Jin, Xitong Zhang, Yinpeng Chen +3
This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. Thi…
A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams
Yifeng Zhao, Zicheng Liu, Pei Zhang +2
A better understanding of dispersion in natural streams requires knowledge of longitudinal dispersion coefficient(LDC). Various methods have been proposed for predictions of LDC. T…
Stronger NAS with Weaker Predictors
Junru Wu, Xiyang Dai, Dongdong Chen +7
Neural Architecture Search (NAS) often trains and evaluates a large number of architectures. Recent predictor-based NAS approaches attempt to alleviate such heavy computation costs…