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pro vyhledávání: '"CHOI, JINHO"'
Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mechanisms take place during adaptation. Here we develop a new Sparse Aut
Externí odkaz:
http://arxiv.org/abs/2412.05276
This study introduces an innovative approach for adaptive power allocation in Non-Orthogonal Multiple Access (NOMA) systems, enhanced by the integration of spaceborne and terrestrial signals through a Reconfigurable Intelligent Surface (RIS). We deve
Externí odkaz:
http://arxiv.org/abs/2410.11254
Low Probability of Detection (LPD) communication aims to obscure the presence of radio frequency (RF) signals to evade surveillance. In the context of mobile surveillance utilizing unmanned aerial vehicles (UAVs), achieving LPD communication presents
Externí odkaz:
http://arxiv.org/abs/2409.17048
The Koopman autoencoder, a data-driven technique, has gained traction for modeling nonlinear dynamics using deep learning methods in recent years. Given the linear characteristics inherent to the Koopman operator, controlling its eigenvalues offers a
Externí odkaz:
http://arxiv.org/abs/2408.11303
The challenge of defining a slot schema to represent the state of a task-oriented dialogue system is addressed by Slot Schema Induction (SSI), which aims to automatically induce slots from unlabeled dialogue data. Whereas previous approaches induce s
Externí odkaz:
http://arxiv.org/abs/2408.01638
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models
The task of Text-to-SQL enables anyone to retrieve information from SQL databases using natural language. Despite several challenges, recent models have made remarkable advancements in this task using large language models (LLMs). Interestingly, we f
Externí odkaz:
http://arxiv.org/abs/2407.07313
Autor:
Finch, Sarah E., Choi, Jinho D.
Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from dialogue contexts in order to im
Externí odkaz:
http://arxiv.org/abs/2406.09138
Autor:
Choi, Jinho
In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal
Externí odkaz:
http://arxiv.org/abs/2406.03779
Autor:
Choi, Jinho
Low Earth orbit (LEO) satellites play a crucial role in providing global connectivity for non-terrestrial networks (NTNs) and supporting various Internet-of-Remote-Things (IoRT) applications. Each LEO satellite functions as a relay node in the sky, e
Externí odkaz:
http://arxiv.org/abs/2405.16483
Autor:
Finch, James D., Choi, Jinho D.
We demonstrate substantial performance gains in zero-shot dialogue state tracking (DST) by enhancing training data diversity through synthetic data generation. Existing DST datasets are severely limited in the number of application domains and slot t
Externí odkaz:
http://arxiv.org/abs/2405.12468