Search Results for author:
Found 22 papers, 15 papers with code
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data.
Graph Signal Processing for Cross-Domain Recommendation
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem.
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information.
Ranked #1 on
Graph Classification
on REDDIT-BINARY
SVD-AE: Simple Autoencoders for Collaborative Filtering
However, existing methods still have room to improve the trade-offs among accuracy, efficiency, and robustness.
Ranked #1 on
Recommendation Systems
on Yelp2018
(HR@10 metric)
Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering
In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues.
QoS-Aware Graph Contrastive Learning for Web Service Recommendation
We propose a novel approach called QoS-aware graph contrastive learning (QAGCL) for web service recommendation.
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
In this paper, inspired by the reaction-diffusion equation, we propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL).
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
These methods are typically designed to i) reduce the computational cost in solving physical dynamics and/or ii) propose techniques to enhance the solution accuracy in fluid and rigid body dynamics.
Ranked #1 on
Physical Simulations
on Deformable Plate
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention
In the SR domain, we, for the first time, show that the same problem occurs.
Ranked #1 on
Sequential Recommendation
on LastFM
Polynomial-based Self-Attention for Table Representation learning
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning.
Graph Convolutions Enrich the Self-Attention in Transformers!
We propose a graph-filter-based self-attention (GFSA) to learn a general yet effective one, whose complexity, however, is slightly larger than that of the original self-attention mechanism.
Ranked #1 on
Speech Recognition
on LibriSpeech 100h test-other
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.
Graph Neural Rough Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Ranked #5 on
Traffic Prediction
on PeMSD3
Climate modeling with neural advection–diffusion equation
Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models.
GREAD: Graph Neural Reaction-Diffusion Networks
In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem.
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).
Blurring-Sharpening Process Models for Collaborative Filtering
Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.
Ranked #1 on
Collaborative Filtering
on Amazon-Book
Prediction-based One-shot Dynamic Parking Pricing
Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.
Graph Neural Controlled Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Linear, or Non-Linear, That is the Question!
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.
Climate Modeling with Neural Diffusion Equations
On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF).
Ranked #1 on
Recommendation Systems
on Amazon-book
