Zobrazeno 1 - 10
of 8 342
pro vyhledávání: '"Ranade, A."'
Human-computer interaction (HCI) has been a widely researched area for many years, with continuous advancements in technology leading to the development of new techniques that change the way we interact with computers. With the recent advent of power
Externí odkaz:
http://arxiv.org/abs/2411.04263
Autor:
Anderson, Eric, Fritz, Jonathan, Lee, Austin, Li, Bohou, Lindblad, Mark, Lindeman, Henry, Meyer, Alex, Parmar, Parth, Ranade, Tanvi, Shah, Mehul A., Sowell, Benjamin, Tecuci, Dan, Thapliyal, Vinayak, Welsh, Matt
LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and us
Externí odkaz:
http://arxiv.org/abs/2409.00847
Autor:
Nidhan, Sheel, Jiang, Haoliang, Ghule, Lalit, Umphrey, Clancy, Ranade, Rishikesh, Pathak, Jay
In this paper, we propose a domain-decomposition-based deep learning (DL) framework, named transient-CoMLSim, for accurately modeling unsteady and nonlinear partial differential equations (PDEs). The framework consists of two key components: (a) a co
Externí odkaz:
http://arxiv.org/abs/2408.14461
Autor:
Zou, Zongren, Kahana, Adar, Zhang, Enrui, Turkel, Eli, Ranade, Rishikesh, Pathak, Jay, Karniadakis, George Em
We extend a recently proposed machine-learning-based iterative solver, i.e. the hybrid iterative transferable solver (HINTS), to solve the scattering problem described by the Helmholtz equation in an exterior domain with a complex absorbing boundary
Externí odkaz:
http://arxiv.org/abs/2405.12380
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the challenge of scaling edge-based graph neural networks as the number of no
Externí odkaz:
http://arxiv.org/abs/2402.15106
Autor:
Ranade, Priyanka, Joshi, Anupam
Publikováno v:
2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Narrative construction is the process of representing disparate event information into a logical plot structure that models an end to end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction
Externí odkaz:
http://arxiv.org/abs/2310.13848
Scaling Studies for Efficient Parameter Search and Parallelism for Large Language Model Pre-training
Autor:
Benington, Michael, Phan, Leo, Paul, Chris Pierre, Shoemaker, Evan, Ranade, Priyanka, Collett, Torstein, Perez, Grant Hodgson, Krieger, Christopher
Publikováno v:
Supercomputing 2023 (SC23) Student Research Poster Track
AI accelerator processing capabilities and memory constraints largely dictate the scale in which machine learning workloads (e.g., training and inference) can be executed within a desirable time frame. Training a state of the art, transformer-based m
Externí odkaz:
http://arxiv.org/abs/2310.05350
Publikováno v:
Journal of Clinical and Diagnostic Research, Vol 18, Iss 11, Pp 13-16 (2024)
Introduction: The Gleason Score (GS) is the most powerful prognostic indicator in prostatic carcinoma. The assignment of the GS is based on the histopathologic patterns of prostatic adenocarcinoma, which are classified according to the Gleason Patter
Externí odkaz:
https://doaj.org/article/bb3f1f1dfa2447c79d4782b1c244eaa9
This paper proposes a stochastic block model with dynamics where the population grows using preferential attachment. Nodes with higher weighted degree are more likely to recruit new nodes, and nodes always recruit nodes from their own community. This
Externí odkaz:
http://arxiv.org/abs/2307.13713
In a preliminary attempt to address the problem of data scarcity in physics-based machine learning, we introduce a novel methodology for data generation in physics-based simulations. Our motivation is to overcome the limitations posed by the limited
Externí odkaz:
http://arxiv.org/abs/2306.11075