Search Results for author:
Found 9 papers, 2 papers with code
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
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.
Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i. e., neural controlled differential equations (NCDEs).
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).
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.
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e. g., every month, iii) trains our time-series forecasting model with the extracted time- series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time- series data, i. e., neural controlled differential equations (NCDEs).
