7 papers
Personalized Parameter-Efficient Fine-Tuning of Foundation Models for Multimodal Recommendation
Sunwoo Kim, Hyunjin Hwang, Kijung Shin
In recent years, substantial research has integrated multimodal item metadata into recommender systems, often by using pre-trained multimodal foundation models to encode such data.…
Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
Sunwoo Kim, Soo Yong Lee, Kyungho Kim +3
Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information…
Emergence of psychopathological computations in large language models
Soo Yong Lee, Hyunjin Hwang, Taekwan Kim +5
Can large language models (LLMs) instantiate computations of psychopathology? An effective approach to the question hinges on addressing two factors. First, for conceptual validity…
Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
Sunwoo Kim, Hyunjin Hwang, Kijung Shin
The performance of a deep learning model on a specific task and dataset depends heavily on its neural architecture, motivating considerable efforts to rapidly and accurately identi…
HyperSearch: Prediction of New Hyperedges through Unconstrained yet Efficient Search
Hyunjin Choo, Fanchen Bu, Hyunjin Hwang +2
Higher-order interactions (HOIs) in complex systems, such as scientific collaborations, multi-protein complexes, and multi-user communications, are commonly modeled as hypergraphs,…
TiGer: Self-Supervised Purification for Time-evolving Graphs
Hyeonsoo Jo, Jongha Lee, Fanchen Bu +1
Time-evolving graphs, such as social and citation networks, often contain noise that distorts structural and temporal patterns, adversely affecting downstream tasks, such as node c…