Zobrazeno 1 - 10
of 55 467
pro vyhledávání: '"Wang, Chao"'
Autor:
Wang, Chao, Thiery, Alexandre H.
Publikováno v:
Inverse Problems, 2024
Proper regularization is crucial in inverse problems to achieve high-quality reconstruction, even with an ill-conditioned measurement system. This is particularly true for three-dimensional photoacoustic tomography, which is computationally demanding
Externí odkaz:
http://arxiv.org/abs/2409.16564
Graph Neural Networks (GNNs) have garnered significant attention and have been extensively utilized across various domains. However, similar to other deep learning models, GNNs are often viewed as black-box models, making it challenging to interpret
Externí odkaz:
http://arxiv.org/abs/2409.15698
Simulating strongly-correlated quantum many-body systems at finite temperatures is a significant challenge in computational physics. In this work, we present a scalable finite-temperature tensor network algorithm for two-dimensional quantum many-body
Externí odkaz:
http://arxiv.org/abs/2409.05285
This study introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing the union of active tra
Externí odkaz:
http://arxiv.org/abs/2409.15320
Autor:
Wu, Jiaxing, Ning, Lin, Liu, Luyang, Lee, Harrison, Wu, Neo, Wang, Chao, Prakash, Sushant, O'Banion, Shawn, Green, Bradley, Xie, Jun
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on the ability to effectively leverage extensive, long user historical data
Externí odkaz:
http://arxiv.org/abs/2409.04421
Fully Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. However, FHE is often hindered by significant performance overhead, particularly for high-precision and complex dat
Externí odkaz:
http://arxiv.org/abs/2409.03568
We propose a method for formally certifying and quantifying individual fairness of deep neural networks (DNN). Individual fairness guarantees that any two individuals who are identical except for a legally protected attribute (e.g., gender or race) r
Externí odkaz:
http://arxiv.org/abs/2409.03220
Autor:
Wang, Chao, Wu, Neo, Ning, Lin, Wu, Jiaxing, Liu, Luyang, Xie, Jun, O'Banion, Shawn, Green, Bradley
Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and therefore are invaluabl
Externí odkaz:
http://arxiv.org/abs/2408.16966
Autor:
Janiczek, John, Chong, Dading, Dai, Dongyang, Faria, Arlo, Wang, Chao, Wang, Tao, Liu, Yuzong
A text-to-speech (TTS) model trained to reconstruct speech given text tends towards predictions that are close to the average characteristics of a dataset, failing to model the variations that make human speech sound natural. This problem is magnifie
Externí odkaz:
http://arxiv.org/abs/2408.15916