Search Results for author: Haoran Liu

Found 7 papers, 4 papers with code

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

1 code implementation17 Jun 2022 Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji

To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood.

Exploring the Common Principal Subspace of Deep Features in Neural Networks

no code implementations6 Oct 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.

Image Reconstruction Self-Supervised Learning

Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models

1 code implementation29 Sep 2021 Meng Liu, Haoran Liu, Shuiwang Ji

In this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS) to alleviate the above limitations.

Graph Generation

Empirical Studies on the Convergence of Feature Spaces in Deep Learning

no code implementations1 Jan 2021 Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou

While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.

Image Reconstruction Self-Supervised Learning

Neural Diffusion Model for Microscopic Cascade Prediction

1 code implementation21 Dec 2018 Cheng Yang, Maosong Sun, Haoran Liu, Shiyi Han, Zhiyuan Liu, Huanbo Luan

The strong assumptions oversimplify the complex diffusion mechanism and prevent these models from better fitting real-world cascade data.

Social and Information Networks Physics and Society

A Blended Deep Learning Approach for Predicting User Intended Actions

no code implementations11 Oct 2018 Fei Tan, Zhi Wei, Jun He, Xiang Wu, Bo Peng, Haoran Liu, Zhenyu Yan

In this work, we focus on pre- dicting attrition, which is one of typical user intended actions.

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