Zobrazeno 1 - 10
of 12 532
pro vyhledávání: '"Um P"'
We present a novel methodology for modeling the influence of the unresolved scales of turbulence for sub-grid modeling. Our approach employs the differentiable physics paradigm in deep learning, allowing a neural network to interact with the differen
Externí odkaz:
http://arxiv.org/abs/2411.13194
Autor:
Um, Soobin, Ye, Jong Chul
We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined as ones living on low-density regions of text-conditional data distri
Externí odkaz:
http://arxiv.org/abs/2410.07838
Autor:
Jung, Jee-weon, Wu, Yihan, Wang, Xin, Kim, Ji-Hoon, Maiti, Soumi, Matsunaga, Yuta, Shim, Hye-jin, Tian, Jinchuan, Evans, Nicholas, Chung, Joon Son, Zhang, Wangyou, Um, Seyun, Takamichi, Shinnosuke, Watanabe, Shinji
This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-world conditions and spoofing attacks generated by Text-To-Speech (TTS
Externí odkaz:
http://arxiv.org/abs/2409.17285
Autor:
Jung, Jee-weon, Zhang, Wangyou, Maiti, Soumi, Wu, Yihan, Wang, Xin, Kim, Ji-Hoon, Matsunaga, Yuta, Um, Seyun, Tian, Jinchuan, Shim, Hye-jin, Evans, Nicholas, Chung, Joon Son, Takamichi, Shinnosuke, Watanabe, Shinji
Text-to-speech (TTS) systems are traditionally trained using modest databases of studio-quality, prompted or read speech collected in benign acoustic environments such as anechoic rooms. The recent literature nonetheless shows efforts to train TTS sy
Externí odkaz:
http://arxiv.org/abs/2409.08711
Autor:
Um, Soobin, Ye, Jong Chul
We present a novel approach for generating minority samples that live on low-density regions of a data manifold. Our framework is built upon diffusion models, leveraging the principle of guided sampling that incorporates an arbitrary energy-based gui
Externí odkaz:
http://arxiv.org/abs/2407.11555
This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled computing in the network (COIN) a
Externí odkaz:
http://arxiv.org/abs/2407.12011
Researchers have focused on understanding how individual's behavior is influenced by the behaviors of their peers in observational studies of social networks. Identifying and estimating causal peer influence, however, is challenging due to confoundin
Externí odkaz:
http://arxiv.org/abs/2405.14789
Autor:
Appana, Raja Abhishek, Idrissi, Faissal El, Ramesh, Prashanth, Canova, Marcello, Kang, Chun Yong, Um, Kimoon
Understanding battery degradation in electric vehicles (EVs) under real-world conditions remains a critical yet under-explored area of research. Central to this investigation is the challenge of estimating the specific degradation modes in aged cells
Externí odkaz:
http://arxiv.org/abs/2405.10857
This paper addresses the problem of minimizing latency with partial computation offloading within Industrial Internet-of-Things (IoT) systems in in-network computing (COIN)-assisted Multiaccess Edge Computing (C-MEC) via ultra-reliable and low latenc
Externí odkaz:
http://arxiv.org/abs/2407.01540
The goal of the multi-sound source localization task is to localize sound sources from the mixture individually. While recent multi-sound source localization methods have shown improved performance, they face challenges due to their reliance on prior
Externí odkaz:
http://arxiv.org/abs/2403.17420