175 citations · 293 across the 11 of their papers we have counts for
11 papers
LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendation
Jiarui Zhu, Jun Hou, Penghang Yu +2
Multimedia recommendation aims to predict users' future behaviors based on observed behaviors and item content information. However, the inherent noise contained in observed behavi…
A Generalized Density Dissipation for Weakly-compressible SPH
Bo Xue Zheng, Zhi Wen Cai, Pei Dong Zhao +3
The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To addr…
Multi-View Graph Convolutional Network for Multimedia Recommendation
Penghang Yu, Zhiyi Tan, Guanming Lu +1
Multimedia recommendation has received much attention in recent years. It models user preferences based on both behavior information and item multimodal information. Though current…
Linear TreeShap
Peng Yu, Chao Xu, Albert Bifet +1
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting…
A machine learning based method to generate random packed isotropic porous media with desired porosity and permeability
Jianhui Li, Tingting Tang, Shimin Yu +1
Porous materials are used in many fields, including energy industry, agriculture, medical industry, etc. The generation of digital porous media facilitates the fabrication of real…
Multiplex Heterogeneous Graph Convolutional Network
Pengyang Yu, Chaofan Fu, Yanwei Yu +3
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to…
The reconstructed thermal lattice Boltzmann flux solver and its applications for simulations of thermal flows
Jinhua Lu, Chuanshan Dai, Peng Yu
The thermal lattice Boltzmann flux solver (TLBFS) has been proposed to overcome the drawbacks of the thermal lattice Boltzmann models. However, as a weakly compressible model, its…
On a scalable problem transformation method for multi-label learning
Dora Jambor, Peng Yu
Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label. A common challenge with this in real-wo…
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification
Donglai Zhu, Hengshuai Yao, Bei Jiang +1
In deep neural network, the cross-entropy loss function is commonly used for classification. Minimizing cross-entropy is equivalent to maximizing likelihood under assumptions of un…
Time Resource Networks
Szymon Sidor, Peng Yu, Cheng Fang +1
The problem of scheduling under resource constraints is widely applicable. One prominent example is power management, in which we have a limited continuous supply of power but must…
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
Shandian Zhe, Zenglin Xu, Yuan Qi
Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)t…