Zobrazeno 1 - 10
of 65 320
pro vyhledávání: '"A Murali"'
A deep learning based method with Convolutional Neural Network (CNN) algorithm is developed for simultaneous determination of the Elliptic Flow coefficient ($v_{2}$) and the Impact Parameter in Heavy-Ion Collisions at relativistic energies. The propo
Externí odkaz:
http://arxiv.org/abs/2411.11001
Autor:
Chitty-Venkata, Krishna Teja, Raskar, Siddhisanket, Kale, Bharat, Ferdaus, Farah, Tanikanti, Aditya, Raffenetti, Ken, Taylor, Valerie, Emani, Murali, Vishwanath, Venkatram
Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring e
Externí odkaz:
http://arxiv.org/abs/2411.00136
Autor:
Zhang, Chaoyun, Yao, Randolph, Qin, Si, Li, Ze, Agrawal, Shekhar, Mishra, Binit R., Tran, Tri, Ma, Minghua, Lin, Qingwei, Chintalapati, Murali, Zhang, Dongmei
The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability and reliability of cloud services, resulting in negative customer experiences. Effectively addressing u
Externí odkaz:
http://arxiv.org/abs/2410.17709
Autor:
Huang, Huang, Sundaralingam, Balakumar, Mousavian, Arsalan, Murali, Adithyavairavan, Goldberg, Ken, Fox, Dieter
Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-
Externí odkaz:
http://arxiv.org/abs/2410.16727
As cloud-based ML expands, ensuring data security during training and inference is critical. GPU-based Trusted Execution Environments (TEEs) offer secure, high-performance solutions, with CPU TEEs managing data movement and GPU TEEs handling authenti
Externí odkaz:
http://arxiv.org/abs/2410.15240
In this work, we consider the problem of learning end to end perception to control for ground vehicles solely from aerial imagery. Photogrammetric simulators allow the synthesis of novel views through the transformation of pre-generated assets into n
Externí odkaz:
http://arxiv.org/abs/2410.14177
When annotators disagree, predicting the labels given by individual annotators can capture nuances overlooked by traditional label aggregation. We introduce three approaches to predicting individual annotator ratings on the toxicity of text by incorp
Externí odkaz:
http://arxiv.org/abs/2410.12217
Autor:
Abreu, Rui, Murali, Vijayaraghavan, Rigby, Peter C, Maddila, Chandra, Sun, Weiyan, Ge, Jun, Chinniah, Kaavya, Mockus, Audris, Mehta, Megh, Nagappan, Nachiappan
Release engineering has traditionally focused on continuously delivering features and bug fixes to users, but at a certain scale, it becomes impossible for a release engineering team to determine what should be released. At Meta's scale, the responsi
Externí odkaz:
http://arxiv.org/abs/2410.06351
As robots become increasingly capable, users will want to describe high-level missions and have robots fill in the gaps. In many realistic settings, pre-built maps are difficult to obtain, so execution requires exploration and mapping that are necess
Externí odkaz:
http://arxiv.org/abs/2410.03035
Although Large Language Models (LLMs) have achieved remarkable performance in numerous downstream tasks, their ubiquity has raised two significant concerns. One is that LLMs can hallucinate by generating content that contradicts relevant contextual i
Externí odkaz:
http://arxiv.org/abs/2410.03026