Search Results for author: Yimeng Min

Found 7 papers, 3 papers with code

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks

no code implementations22 Jan 2022 Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, Guy Wolf

However, current GNN models (and GCNs in particular) are known to be constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets.

Approximating Excited States using Neural Networks

1 code implementation24 Dec 2020 Yimeng Min

Recently developed neural network-based wave function methods are capable of achieving state-of-the-art results for finding the ground state in real space.

Computational Physics Disordered Systems and Neural Networks

Geometric Scattering Attention Networks

1 code implementation28 Oct 2020 Yimeng Min, Frederik Wenkel, Guy Wolf

Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning.

Graph Representation Learning Node Classification

Persistent Neurons

no code implementations2 Jul 2020 Yimeng Min

More precisely, we utilize the end of trajectories and let the parameters explore new landscapes by penalizing the model from converging to the previous solutions under the same initialization.

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

1 code implementation NeurIPS 2020 Yimeng Min, Frederik Wenkel, Guy Wolf

Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features.

Graph Attention Node Classification

Predicting ice flow using machine learning

no code implementations20 Oct 2019 Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio

Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered.

Frame Optical Flow Estimation +1

Beyond Classical Diffusion: Ballistic Graph Neural Network

no code implementations25 Sep 2019 Yimeng Min

The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$).

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