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pro vyhledávání: '"P., Deepak"'
We revisit the problem of estimating the mean of a high-dimensional distribution in the presence of an $\varepsilon$-fraction of adversarial outliers. When $\varepsilon$ is at most some sufficiently small constant, previous works can achieve optimal
Externí odkaz:
http://arxiv.org/abs/2411.14305
Autor:
Shaw, Kenneth, Li, Yulong, Yang, Jiahui, Srirama, Mohan Kumar, Liu, Ray, Xiong, Haoyu, Mendonca, Russell, Pathak, Deepak
To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However,
Externí odkaz:
http://arxiv.org/abs/2411.13677
Autor:
Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, Nagpurkar, Priya
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co
Externí odkaz:
http://arxiv.org/abs/2411.13239
Autor:
Ravikumar, Deepak, Yeo, Alex, Zhu, Yiwen, Lakra, Aditya, Nagulapalli, Harsha, Ravindran, Santhosh Kumar, Suh, Steve, Dutta, Niharika, Fogarty, Andrew, Park, Yoonjae, Khushalani, Sumeet, Tarafdar, Arijit, Parekh, Kunal, Krishnan, Subru
Publikováno v:
Proceedings of the VLDB Endowment, Vol. 17, No. 7 ISSN 2150-8097, 2024
The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. At Microsoft, we aim at p
Externí odkaz:
http://arxiv.org/abs/2411.11326
Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate this question and find that the features and representations learned du
Externí odkaz:
http://arxiv.org/abs/2411.09702
Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequen
Externí odkaz:
http://arxiv.org/abs/2411.09283
The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional implant design methods rely mainly on manual processes, making them time-
Externí odkaz:
http://arxiv.org/abs/2411.09204
Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data, presenting significant challenges for traditional centralized machine learning models due to substantial communication costs and privacy risks. Federate
Externí odkaz:
http://arxiv.org/abs/2411.06352
Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and req
Externí odkaz:
http://arxiv.org/abs/2411.05961
Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning
Externí odkaz:
http://arxiv.org/abs/2411.04112