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The Falcon Series of Open Language Models
arXiv.org, 2023We introduce the Falcon series: 7B, 40B, and 180B parameters causal decoder-only models trained on a diverse high-quality corpora predominantly assembled from web data. The largest model, Falcon-180B, has been trained on over 3.5 trillion tokens of text--
Ebtesam Almazrouei +11 more
semanticscholar +1 more source
Proceedings of the 3rd P4 Workshop in Europe, 2020
We present Falcon, a novel scheduler design for large scale data analytics workloads. To improve the quality of the scheduling decisions, Falcon uses a single central scheduler. To scale the central scheduler to support large clusters, Falcon offloads the scheduling operation to a programmable switch.
Ibrahim Kettaneh +4 more
openaire +2 more sources
We present Falcon, a novel scheduler design for large scale data analytics workloads. To improve the quality of the scheduling decisions, Falcon uses a single central scheduler. To scale the central scheduler to support large clusters, Falcon offloads the scheduling operation to a programmable switch.
Ibrahim Kettaneh +4 more
openaire +2 more sources
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, 2010
Concurrency fault are difficult to find because they usually occur under specific thread interleavings. Fault-detection tools in this area find data-access patterns among thread interleavings, but they report benign patterns as well as actual faulty patterns.
Sangmin Park +2 more
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Concurrency fault are difficult to find because they usually occur under specific thread interleavings. Fault-detection tools in this area find data-access patterns among thread interleavings, but they report benign patterns as well as actual faulty patterns.
Sangmin Park +2 more
openaire +2 more sources
Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System
Proceedings of the VLDB Endowment, 2023Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models without disclosing their raw data. In the vertical federated learning (VFL) setting, the collaborating parties have data from the same set of users
Yuncheng Wu, Naili Xing
semanticscholar +1 more source
Proceedings of the 2017 ACM International Conference on Management of Data, 2017
Many works have applied crowdsourcing to entity matching (EM). While promising, these approaches are limited in that they often require a developer to be in the loop. As such, it is difficult for an organization to deploy multiple crowdsourced EM solutions, because there are simply not enough developers.
Sanjib Das +8 more
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Many works have applied crowdsourcing to entity matching (EM). While promising, these approaches are limited in that they often require a developer to be in the loop. As such, it is difficult for an organization to deploy multiple crowdsourced EM solutions, because there are simply not enough developers.
Sanjib Das +8 more
openaire +1 more source
FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation
arXiv.orgHumanoid loco-manipulation holds transformative potential for daily service and industrial tasks, yet achieving precise, robust whole-body control with 3D end-effector force interaction remains a major challenge.
Yuanhang Zhang +8 more
semanticscholar +1 more source