Zobrazeno 1 - 10
of 6 815
pro vyhledávání: '"Huang, Po"'
Autor:
Xu, Hu, Huang, Po-Yao, Tan, Xiaoqing Ellen, Yeh, Ching-Feng, Kahn, Jacob, Jou, Christine, Ghosh, Gargi, Levy, Omer, Zettlemoyer, Luke, Yih, Wen-tau, Li, Shang-Wen, Xie, Saining, Feichtenhofer, Christoph
This paper focuses on creating synthetic data to improve the quality of image captions. Existing works typically have two shortcomings. First, they caption images from scratch, ignoring existing alt-text metadata, and second, lack transparency if the
Externí odkaz:
http://arxiv.org/abs/2410.17251
Quantum circuit simulation is important in the evolution of quantum software and hardware. Novel algorithms can be developed and evaluated by performing quantum circuit simulations on classical computers before physical quantum computers are availabl
Externí odkaz:
http://arxiv.org/abs/2410.09326
The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architectur
Externí odkaz:
http://arxiv.org/abs/2410.04435
Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we manipulate input speech to sound as though it was recorded within a differ
Externí odkaz:
http://arxiv.org/abs/2409.14340
Geospatial data come from various sources, such as satellites, aircraft, and LiDAR. The variability of the source is not limited to the types of data acquisition techniques, as we have maps from different time periods. To incorporate these data for a
Externí odkaz:
http://arxiv.org/abs/2408.14152
Autor:
Huang, Po-Hsuan, Shao, Hsuan-Lei
The study of word co-occurrence networks has attracted the attention of researchers due to their potential significance as well as applications. Understanding the structure of word co-occurrence networks is therefore important to fully realize their
Externí odkaz:
http://arxiv.org/abs/2408.09404
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sa
Externí odkaz:
http://arxiv.org/abs/2408.05116
Autor:
Ghalebikesabi, Sahra, Bagdasaryan, Eugene, Yi, Ren, Yona, Itay, Shumailov, Ilia, Pappu, Aneesh, Shi, Chongyang, Weidinger, Laura, Stanforth, Robert, Berrada, Leonard, Kohli, Pushmeet, Huang, Po-Sen, Balle, Borja
Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents,
Externí odkaz:
http://arxiv.org/abs/2408.02373
Autor:
Maldonado-Lopez, Daniel, Huang, Po-Wei, Sanchez-Lievanos, Karla R., Jana, Gourhari, Mendoza-Cortes, Jose L., Knowles, Kathryn E., Hatzell, Marta C.
Publikováno v:
Adv. Funct. Mater. 2024, 2413319
Many ligands commonly used to prepare nanoparticle catalysts with precise nanoscale features contain nitrogen (e.g., oleylamine); here, we found that the use of nitrogen-containing ligands during the synthesis of metal oxide nanoparticle catalysts su
Externí odkaz:
http://arxiv.org/abs/2408.01371
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on the loss distribution of training samples. However, they often neglect
Externí odkaz:
http://arxiv.org/abs/2407.07331