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pro vyhledávání: '"P., Sahni"'
Autor:
Hsu, Pin-Lun, Dai, Yun, Kothapalli, Vignesh, Song, Qingquan, Tang, Shao, Zhu, Siyu, Shimizu, Steven, Sahni, Shivam, Ning, Haowen, Chen, Yanning
Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performance. In this work, we introduce Liger-Kernel, an open-sourced set of T
Externí odkaz:
http://arxiv.org/abs/2410.10989
In finance, Random Matrix Theory (RMT) is an important tool for filtering out noise from large datasets, revealing true correlations among stocks, enhancing risk management and portfolio optimization. In this study, we use RMT to filter out noise fro
Externí odkaz:
http://arxiv.org/abs/2410.07947
Autor:
Ma, Jenny, Sreedhar, Karthik, Liu, Vivian, Wang, Sitong, Perez, Pedro Alejandro, Sahni, Riya, Chilton, Lydia B.
Recent advancements in large language models have significantly expedited the process of generating front-end code. This allows users to rapidly prototype user interfaces and ideate through code, a process known as exploratory programming. However, e
Externí odkaz:
http://arxiv.org/abs/2410.00400
The hyperbolic network models exhibit very fundamental and essential features, like small-worldness, scale-freeness, high-clustering coefficient, and community structure. In this paper, we comprehensively explore the presence of an important feature,
Externí odkaz:
http://arxiv.org/abs/2406.19953
Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data di
Externí odkaz:
http://arxiv.org/abs/2405.19136
Autor:
Chan, Trevor J., Sahni, Aarush, Fang, Yijin, Li, Jie, Luthra, Alisha, Pouch, Alison, Rajapakse, Chamith S.
We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation of 3D images building on the existing Segment Anything Model. We achieve fast and accurate segmentations in 3D images with a four-step strategy involving: user prompting with
Externí odkaz:
http://arxiv.org/abs/2405.06786
This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of stocks belonging to the periphery part of the Planar maximally filtere
Externí odkaz:
http://arxiv.org/abs/2405.12993
In this study, we model the Indian stock market as heterogenous scale free network, which is then embedded in a two dimensional hyperbolic space through a machine learning based technique called as coalescent embedding. This allows us to apply the hy
Externí odkaz:
http://arxiv.org/abs/2404.04710
Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy,
Externí odkaz:
http://arxiv.org/abs/2403.15815
Publikováno v:
2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Toronto, ON, Canada, 2023, pp. 28-36
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fundamentally important problem, decides when and where microservices ar
Externí odkaz:
http://arxiv.org/abs/2403.08687