Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Park, Noseong"'
Autor:
Kang, Mingu, Lee, Dongseok, Cho, Woojin, Park, Jaehyeon, Lee, Kookjin, Gruber, Anthony, Hong, Youngjoon, Park, Noseong
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 pre-training techniques. Motivated by this, we explore whether a simila
Externí odkaz:
http://arxiv.org/abs/2410.06442
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR). The approach exploits the low rank st
Externí odkaz:
http://arxiv.org/abs/2410.04001
Autor:
Lim, Seonkyu, Choi, Jeongwhan, Park, Noseong, Yoon, Sang-Ha, Kang, ShinHyuck, Kim, Young-Min, Kang, Hyunjoong
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 by government agencies for GDP nowcasting due to their ability to hand
Externí odkaz:
http://arxiv.org/abs/2409.08732
Autor:
Cho, Woojin, Jo, Minju, Lim, Haksoo, Lee, Kookjin, Lee, Dongeun, Hong, Sanghyun, Park, Noseong
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 as design optimization or uncertainty quantification, solutions of th
Externí odkaz:
http://arxiv.org/abs/2408.09446
Autor:
Choi, Kanghyun, Lee, Hye Yoon, Kwon, Dain, Park, SunJong, Kim, Kyuyeun, Park, Noseong, Lee, Jinho
Data-free quantization (DFQ) is a technique that creates a lightweight network from its full-precision counterpart without the original training data, often through a synthetic dataset. Although several DFQ methods have been proposed for vision trans
Externí odkaz:
http://arxiv.org/abs/2407.20021
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendati
Externí odkaz:
http://arxiv.org/abs/2407.12374
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 forecasting models are trained using the mean squared error (MSE). However, t
Externí odkaz:
http://arxiv.org/abs/2407.01622
Autor:
Lee, Hyeyoon, Choi, Kanghyun, Kwon, Dain, Park, Sunjong, Jaiswal, Mayoore Selvarasa, Park, Noseong, Choi, Jonghyun, Lee, Jinho
Recent advances in adversarial robustness rely on an abundant set of training data, where using external or additional datasets has become a common setting. However, in real life, the training data is often kept private for security and privacy issue
Externí odkaz:
http://arxiv.org/abs/2406.15635
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, which impedes the propagation of long-range information. Prior works h
Externí odkaz:
http://arxiv.org/abs/2406.03671
Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approxima
Externí odkaz:
http://arxiv.org/abs/2405.16305