Search Results for author:
Found 8 papers, 2 papers with code
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results.
Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis.
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning
Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients.
Graph Propagation Transformer for Graph Representation Learning
The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks.
Ranked #2 on
Graph Regression
on PCQM4M-LSC
(Validation MAE metric)
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods.
Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction of properties that were not present in the original training data.
Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction
Here this approach is extended for general steerable wavelets which are equivariant to translations and rotations, resulting in a sparse model of the target function.
