32 citations · 58 across the 37 of their papers we have counts for
40 papers
Spatiotemporal Hyperedges for EEG Seizure Detection and Prediction
Hyunju Kim, Sheo Yon Jhin, Noseong Park +1
Seizure detection and prediction from EEG are clinically important but challenging because seizures are rare, temporally localized, and propagate as coordinated events across multi…
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…
One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
Woosung Kang, Jiwon Jeong, Jonghyeok Shin +2
Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution o…
How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators
Jihyeon Hur, Yongseok Kwon, Min-Gi Jo +2
Neural operators have emerged as a powerful data-driven approach for solving time-dependent PDEs. Among recent advances, memory-augmented neural operators explicitly incorporate pa…
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
Jeongwhan Choi, Jongwoo Kim, Woosung Kang +1
One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: th…
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-…
HINTS: Extraction of Human Insights from Time-Series Without External Sources
Sheo Yon Jhin, Noseong Park
Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series…
How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts
Sumin Park, Noseong Park
Finding the optimal configuration of Sparse Mixture-ofExperts (SMoE) that maximizes semantic differentiation among experts is essential for exploiting the full potential of MoE arc…
Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
Jeongwhan Choi, Woosung Kang, Minseo Kim +2
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN for tabular data and…
TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models
Haksoo Lim, Jaehoon Lee, Sewon Park +2
Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data s…
Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
Jeongwhan Choi, Seungjun Park, Sumin Park +2
Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transform…
Learning Low Rank Neural Representations of Hyperbolic Wave Dynamics from Data
Woojin Cho, Kookjin Lee, Noseong Park +2
We present a data-driven dimensionality reduction method that is well-suited for physics-based data representing hyperbolic wave propagation. The method utilizes a specialized neur…
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…
Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Pin-Jui Huang, Yu-Hsuan Liao, SooHeon Kim +3
Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reduc…
PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
Minju Jo, Woojin Cho, Uvini Balasuriya Mudiyanselage +3
Scientific machine learning often involves representing complex solution fields that exhibit high-frequency features such as sharp transitions, fine-scale oscillations, and localiz…
Neural Functions for Learning Periodic Signal
Woojin Cho, Minju Jo, Kookjin Lee +1
As function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to lear…
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…
Possibility for Proactive Anomaly Detection
Jinsung Jeon, Jaehyeon Park, Sewon Park +3
Time-series anomaly detection, which detects errors and failures in a workflow, is one of the most important topics in real-world applications. The purpose of time-series anomaly d…
PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networks
Youn-Yeol Yu, Jeongwhan Choi, Jaehyeon Park +2
Recently, data-driven simulators based on graph neural networks have gained attention in modeling physical systems on unstructured meshes. However, they struggle with long-range de…
Unveiling the Potential of Superexpressive Networks in Implicit Neural Representations
Uvini Balasuriya Mudiyanselage, Woojin Cho, Minju Jo +2
In this study, we examine the potential of one of the ``superexpressive'' networks in the context of learning neural functions for representing complex signals and performing machi…
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
Mingu Kang, Dongseok Lee, Woojin Cho +5
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and p…
FastLRNR and Sparse Physics Informed Backpropagation
Woojin Cho, Kookjin Lee, Noseong Park +2
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Ra…
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
Seonkyu Lim, Jeongwhan Choi, Noseong Park +4
Gross domestic product (GDP) nowcasting is crucial for policy-making as GDP growth is a key indicator of economic conditions. Dynamic factor models (DFMs) have been widely adopted…
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Woojin Cho, Minju Jo, Haksoo Lim +4
Complex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics. In applications such…