32 citations · 34 across the 5 of their papers we have counts for
5 papers
Q-Delta: Beyond Key-Value Associative State Evolution
Sumin Park, Seojin Kim, Noseong Park
Linear attention reformulates sequence modeling as recurrent state evolution, enabling efficient linear-time inference. Under the key-value associative paradigm, existing approache…
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…
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
Sheo Yon Jhin, Seojin Kim, Noseong Park
Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecas…
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…