Zobrazeno 1 - 10
of 20 765
pro vyhledávání: '"P Choo"'
Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, l
Externí odkaz:
http://arxiv.org/abs/2412.13469
Autor:
Cooper, A. Feder, Choquette-Choo, Christopher A., Bogen, Miranda, Jagielski, Matthew, Filippova, Katja, Liu, Ken Ziyu, Chouldechova, Alexandra, Hayes, Jamie, Huang, Yangsibo, Mireshghallah, Niloofar, Shumailov, Ilia, Triantafillou, Eleni, Kairouz, Peter, Mitchell, Nicole, Liang, Percy, Ho, Daniel E., Choi, Yejin, Koyejo, Sanmi, Delgado, Fernando, Grimmelmann, James, Shmatikov, Vitaly, De Sa, Christopher, Barocas, Solon, Cyphert, Amy, Lemley, Mark, boyd, danah, Vaughan, Jennifer Wortman, Brundage, Miles, Bau, David, Neel, Seth, Jacobs, Abigail Z., Terzis, Andreas, Wallach, Hanna, Papernot, Nicolas, Lee, Katherine
We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy. These aspirations are both numerous and varied, mo
Externí odkaz:
http://arxiv.org/abs/2412.06966
Autor:
Jia, Yongzhe, Zhang, Xuyun, Hu, Hongsheng, Choo, Kim-Kwang Raymond, Qi, Lianyong, Xu, Xiaolong, Beheshti, Amin, Dou, Wanchun
Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer f
Externí odkaz:
http://arxiv.org/abs/2412.05823
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
Learning quantum states is a crucial task for realizing the potential of quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that employs reinforc
Externí odkaz:
http://arxiv.org/abs/2412.02334
Traditional deep learning methods in medical imaging often focus solely on segmentation or classification, limiting their ability to leverage shared information. Multi-task learning (MTL) addresses this by combining both tasks through shared represen
Externí odkaz:
http://arxiv.org/abs/2412.00351
Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demand
Externí odkaz:
http://arxiv.org/abs/2411.18664
Autor:
Gwak, Daehoon, Park, Junwoo, Park, Minho, Park, Chaehun, Lee, Hyunchan, Choi, Edward, Choo, Jaegul
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics. However, existing datasets available for this task are oft
Externí odkaz:
http://arxiv.org/abs/2411.14042
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is provided as potentially inaccurate advice on the true, unknown distrib
Externí odkaz:
http://arxiv.org/abs/2411.12700
Autor:
Huang, Shaoying, Algarín, José Miguel, Alonso, Joseba, R, Anieyrudh, Borreguero, Jose, Bschorr, Fabian, Cassidy, Paul, Choo, Wei Ming, Corcos, David, Guallart-Naval, Teresa, Han, Heng Jing, Igwe, Kay Chioma, Kang, Jacob, Li, Joe, Littin, Sebastian, Liu, Jie, Rodriguez, Gonzalo Gabriel, Solomon, Eddy, Tan, Li-Kuo, Tian, Rui, Webb, Andrew, Weber, Susanna, Xiao, Dan, Xu, Minxuan, Yu, Wenwei, Zhang, Zhiyong, Zinghini, Isabelle, Blümich, Bernhard
Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapo
Externí odkaz:
http://arxiv.org/abs/2411.11365