Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Choi, JaeWoong"'
Autor:
Choi, Jaewoong
While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on R&D outcomes, specifically patents, and academic literature. However,
Externí odkaz:
http://arxiv.org/abs/2411.02738
Autor:
Gazdieva, Milena, Choi, Jaemoo, Kolesov, Alexander, Choi, Jaewoong, Mokrov, Petr, Korotin, Alexander
A common challenge in aggregating data from multiple sources can be formalized as an \textit{Optimal Transport} (OT) barycenter problem, which seeks to compute the average of probability distributions with respect to OT discrepancies. However, the pr
Externí odkaz:
http://arxiv.org/abs/2410.03974
Optimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution. Recently, several approaches have emerged for learning the optimal transport map for a given cost function using n
Externí odkaz:
http://arxiv.org/abs/2410.03783
Unpaired point cloud completion explores methods for learning a completion map from unpaired incomplete and complete point cloud data. In this paper, we propose a novel approach for unpaired point cloud completion using the unbalanced optimal transpo
Externí odkaz:
http://arxiv.org/abs/2410.02671
Autor:
Choi, Jaemoo, Choi, Jaewoong
The Optimal Transport (OT) problem investigates a transport map that connects two distributions while minimizing a given cost function. Finding such a transport map has diverse applications in machine learning, such as generative modeling and image-t
Externí odkaz:
http://arxiv.org/abs/2410.02656
Despite the usefulness of machine learning approaches for the early screening of potential breakthrough technologies, their practicality is often hindered by opaque models. To address this, we propose an interpretable machine learning approach to pre
Externí odkaz:
http://arxiv.org/abs/2407.16939
Recent studies have increasingly applied natural language processing (NLP) to automatically extract experimental research data from the extensive battery materials literature. Despite the complex process involved in battery manufacturing -- from mate
Externí odkaz:
http://arxiv.org/abs/2407.15459
Machine learning (ML) has revolutionized the digital transformation of technology valuation by predicting the value of patents with high accuracy. However, the lack of validation regarding the reliability of these models hinders experts from fully tr
Externí odkaz:
http://arxiv.org/abs/2406.05446
Autor:
Yi, Gyeong Hoon, Choi, Jiwoo, Song, Hyeongyun, Miano, Olivia, Choi, Jaewoong, Bang, Kihoon, Lee, Byungju, Sohn, Seok Su, Buttler, David, Hiszpanski, Anna, Han, Sang Soo, Kim, Donghun
Efficiently extracting data from tables in the scientific literature is pivotal for building large-scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule-based extractions are an ineffectiv
Externí odkaz:
http://arxiv.org/abs/2406.05431
Wasserstein Gradient Flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization over the probability distributions. Numerically approximating the conti
Externí odkaz:
http://arxiv.org/abs/2402.05443