4 citations · 12 across the 9 of their papers we have counts for
10 papers
Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation?
Hyowon Wi, Noseong Park
In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning t…
Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain
Hyowon Wi, Jeongwhan Choi, Noseong Park
Transformers have demonstrated remarkable performance across diverse domains. The key component of Transformers is self-attention, which learns the relationship between any two tok…
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Jeongwhan Choi, Sumin Park, Hyowon Wi +2
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,…
SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation
Chaejeong Lee, Jeongwhan Choi, Hyowon Wi +2
Graph-based collaborative filtering (CF) has emerged as a promising approach in recommender systems. Despite its achievements, graph-based CF models face challenges due to data spa…
Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
Hyowon Wi, Yehjin Shin, Noseong Park
Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in…
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Jeongwhan Choi, Hyowon Wi, Chaejeong Lee +3
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from ra…
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention
Yehjin Shin, Jeongwhan Choi, Hyowon Wi +1
Sequential recommendation (SR) models based on Transformers have achieved remarkable successes. The self-attention mechanism of Transformers for computer vision and natural languag…
Graph Convolutions Enrich the Self-Attention in Transformers!
Jeongwhan Choi, Hyowon Wi, Jayoung Kim +4
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-ser…
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Seonkyu Lim, Jaehyeon Park, Seojin Kim +5
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 forecasti…
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Seoyoung Hong, Minju Jo, Seungji Kook +4
Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-worl…