Zobrazeno 1 - 10
of 130 203
pro vyhledávání: '"ACTOR, P."'
In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational complexity. To a
Externí odkaz:
http://arxiv.org/abs/2411.15806
This paper introduces TIPS: Threat Actor Informed Prioritization using SecEncoder, a specialized language model for security. TIPS combines the strengths of both encoder and decoder language models to detect and prioritize compromised applications. B
Externí odkaz:
http://arxiv.org/abs/2411.07519
In recent years, the serverless paradigm has been widely adopted to develop cloud applications, as it enables building scalable solutions while delegating operational concerns such as infrastructure management and resource provisioning to the serverl
Externí odkaz:
http://arxiv.org/abs/2410.21793
Text-To-SQL (T2S) conversion based on large language models (LLMs) has found a wide range of applications, by leveraging the capabilities of LLMs in interpreting the query intent expressed in natural language. Existing research focuses on suitable re
Externí odkaz:
http://arxiv.org/abs/2410.22082
The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partiti
Externí odkaz:
http://arxiv.org/abs/2410.15831
Autor:
Gupta, Naman, Kirtania, Shashank, Gupta, Priyanshu, Kariya, Krishna, Gulwani, Sumit, Iyer, Arun, Parthasarathy, Suresh, Radhakrishna, Arjun, Rajamani, Sriram K., Soares, Gustavo
Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these
Externí odkaz:
http://arxiv.org/abs/2410.10584
In this paper, we establish the global convergence of the actor-critic algorithm with a significantly improved sample complexity of $O(\epsilon^{-3})$, advancing beyond the existing local convergence results. Previous works provide local convergence
Externí odkaz:
http://arxiv.org/abs/2410.08868
Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can effectively execute surveillance, connectivity, and computing services to ground users (GUs). These missions require trajectory planning, UAV-GUs association, task offloading, next-
Externí odkaz:
http://arxiv.org/abs/2410.06627
Autor:
Mohammadi, Esmaeel, Ortiz-Arroyo, Daniel, Hansen, Aviaja Anna, Stokholm-Bjerregaard, Mikkel, Gros, Sebastien, Anand, Akhil S, Durdevic, Petar
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportiona
Externí odkaz:
http://arxiv.org/abs/2411.18305
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
The lack of occlusion data in commonly used action recognition video datasets limits model robustness and impedes sustained performance improvements. We construct OccludeNet, a large-scale occluded video dataset that includes both real-world and synt
Externí odkaz:
http://arxiv.org/abs/2411.15729