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pro vyhledávání: '"PARK, DAVID"'
Autor:
Wu, Wei-Cheng, Yan, Yutian, Egilsson, Hallgrimur David, Park, David, Chan, Steven, Hauser, Christophe, Wang, Weihang
WebAssembly is a low-level bytecode language designed for client-side execution in web browsers. The need for decompilation techniques that recover high-level source code from WASM binaries has grown as WASM continues to gain widespread adoption and
Externí odkaz:
http://arxiv.org/abs/2411.02278
Autor:
Park, David K., Ren, Yihui, Kilic, Ozgur O., Korchuganova, Tatiana, Vatsavai, Sairam Sri, Boudreau, Joseph, Chowdhury, Tasnuva, Feng, Shengyu, Khan, Raees, Kim, Jaehyung, Klasky, Scott, Maeno, Tadashi, Nilsson, Paul, Outschoorn, Verena Ingrid Martinez, Podhorszki, Norbert, Suter, Frederic, Yang, Wei, Yang, Yiming, Yoo, Shinjae, Klimentov, Alexei, Hoisie, Adolfy
Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational power for varied tasks, including structured data processing, Monte Ca
Externí odkaz:
http://arxiv.org/abs/2410.07940
Autor:
Real, Esteban, Chen, Yao, Rossini, Mirko, de Souza, Connal, Garg, Manav, Verghese, Akhil, Firsching, Moritz, Le, Quoc V., Cubuk, Ekin Dogus, Park, David H.
Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions. For example, an exponential can be calculated with a Taylor series. These approximation methods were developed over th
Externí odkaz:
http://arxiv.org/abs/2312.08472
Akademický článek
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Autor:
Talwar, Kunal, Wang, Shan, McMillan, Audra, Jina, Vojta, Feldman, Vitaly, Bansal, Pansy, Basile, Bailey, Cahill, Aine, Chan, Yi Sheng, Chatzidakis, Mike, Chen, Junye, Chick, Oliver, Chitnis, Mona, Ganta, Suman, Goren, Yusuf, Granqvist, Filip, Guo, Kristine, Jacobs, Frederic, Javidbakht, Omid, Liu, Albert, Low, Richard, Mascenik, Dan, Myers, Steve, Park, David, Park, Wonhee, Parsa, Gianni, Pauly, Tommy, Priebe, Christian, Rishi, Rehan, Rothblum, Guy, Scaria, Michael, Song, Linmao, Song, Congzheng, Tarbe, Karl, Vogt, Sebastian, Winstrom, Luke, Zhou, Shundong
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their u
Externí odkaz:
http://arxiv.org/abs/2307.15017