research
∙
07/05/2023
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning
Currently, research on Reinforcement learning (RL) can be broadly classi...
share
research
∙
06/08/2023
Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
Directed evolution plays an indispensable role in protein engineering th...
research
∙
04/05/2023
Graph Representation Learning for Interactive Biomolecule Systems
Advances in deep learning models have revolutionized the study of biomol...
research
∙
03/20/2023
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Learning to predict agent motions with relationship reasoning is importa...
research
∙
02/28/2023
Framelet Message Passing
Graph neural networks (GNNs) have achieved champion in wide applications...
research
∙
12/29/2022
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Deep learning has been widely used for protein engineering. However, it ...
research
∙
08/06/2022
Oversquashing in GNNs through the lens of information contraction and graph expansion
The quality of signal propagation in message-passing graph neural networ...
research
∙
06/15/2022
How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images
Gigapixel medical images provide massive data, both morphological textur...
research
∙
06/01/2022
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks
The research for characterizing GNN expressiveness attracts much attenti...
research
∙
06/23/2021
Weisfeiler and Lehman Go Cellular: CW Networks
Graph Neural Networks (GNNs) are limited in their expressive power, stru...
research
∙
06/18/2021
Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention du...
research
∙
03/04/2021
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predomin...
research
∙
12/12/2020
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Graph representation learning has many real-world applications, from sup...
research
∙
08/19/2020
On the Approximation Lower Bound for Neural Nets with Random Weights
A random net is a shallow neural network where the hidden layer is froze...
research
∙
07/22/2020
Graph Neural Networks with Haar Transform-Based Convolution and Pooling: A Complete Guide
Graph Neural Networks (GNNs) have recently caught great attention and ac...
research
∙
07/18/2020
Distributed Learning via Filtered Hyperinterpolation on Manifolds
Learning mappings of data on manifolds is an important topic in contempo...
research
∙
06/29/2020
Path Integral Based Convolution and Pooling for Graph Neural Networks
Graph neural networks (GNNs) extends the functionality of traditional ne...
research
∙
01/31/2020
Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus
The transparent cornea is the window of the eye, facilitating the entry ...
research
∙
01/31/2020
CosmoVAE: Variational Autoencoder for CMB Image Inpainting
Cosmic microwave background radiation (CMB) is critical to the understan...
research
∙
10/06/2019
Distributed filtered hyperinterpolation for noisy data on the sphere
Problems in astrophysics, space weather research and geophysics usually ...
research
∙
09/25/2019
HaarPooling: Graph Pooling with Compressive Haar Basis
Deep Graph Neural Networks (GNNs) are instrumental in graph classificati...
research
∙
07/31/2019
FaVeST: Fast Vector Spherical Harmonic Transforms
Vector spherical harmonics on S^2⊂R^3 have wide applications in geophysi...
research
∙
07/31/2019
Optimization-based quasi-uniform spherical t-design and generalized multitaper for complex physiological time series
Motivated by the demand to analyze complex physiological time series, we...
research
∙
07/31/2019
Fast Tensor Needlet Transforms for Tangent Vector Fields on the Sphere
This paper constructs a semi-discrete tight frame of tensor needlets ass...
research
∙
07/10/2019
Haar Transforms for Graph Neural Networks
Graph Neural Networks (GNNs) have become a topic of intense research rec...
research
∙
01/06/2017