4 citations · 8 across the 7 of their papers we have counts for
7 papers
Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
Yehjin Shin, Seojin Kim, Noseong Park
State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-…
TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
Yehjin Shin, Jeongwhan Choi, Seojin Kim +1
Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models co…
Polynomial-based Self-Attention for Table Representation learning
Jayoung Kim, Yehjin Shin, Jeongwhan Choi +2
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation le…
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…
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…
SOS: Score-based Oversampling for Tabular Data
Jayoung Kim, Chaejeong Lee, Yehjin Shin +4
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (…