Zobrazeno 1 - 10
of 783
pro vyhledávání: '"RASMUSSEN, KIM"'
Autor:
Bhattarai, Manish, Barron, Ryan, Eren, Maksim, Vu, Minh, Grantcharov, Vesselin, Boureima, Ismael, Stanev, Valentin, Matuszek, Cynthia, Valtchinov, Vladimir, Rasmussen, Kim, Alexandrov, Boian
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effectiveness of RAG depends on the relevance of retrieved documents, which
Externí odkaz:
http://arxiv.org/abs/2412.04661
Autor:
Adak, Dibyendu, Truong, Duc P., Vuchkov, Radoslav, De, Saibal, DeSantis, Derek, Roberts, Nathan V., Rasmussen, Kim Ø., Alexandrov, Boian S.
In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent co
Externí odkaz:
http://arxiv.org/abs/2411.04026
Autor:
Barron, Ryan C., Grantcharov, Ves, Wanna, Selma, Eren, Maksim E., Bhattarai, Manish, Solovyev, Nicholas, Tompkins, George, Nicholas, Charles, Rasmussen, Kim Ø., Matuszek, Cynthia, Alexandrov, Boian S.
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific
Externí odkaz:
http://arxiv.org/abs/2410.02721
Autor:
Vu, Minh, Nebgen, Ben, Skau, Erik, Zollicoffer, Geigh, Castorena, Juan, Rasmussen, Kim, Alexandrov, Boian, Bhattarai, Manish
As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its resilience to such attacks is Non-negative Matrix Factor
Externí odkaz:
http://arxiv.org/abs/2408.03909
Autor:
Danis, Mustafa Engin, Truong, Duc P., DeSantis, Derek, Petersen, Mark, Rasmussen, Kim O., Alexandrov, Boian S.
In this paper, we introduce a high-order tensor-train (TT) finite volume method for the Shallow Water Equations (SWEs). We present the implementation of the $3^{rd}$ order Upwind and the $5^{th}$ order Upwind and WENO reconstruction schemes in the TT
Externí odkaz:
http://arxiv.org/abs/2408.03483
Autor:
Wanna, Selma, Barron, Ryan, Solovyev, Nick, Eren, Maksim E., Bhattarai, Manish, Rasmussen, Kim, Alexandrov, Boian S.
Topic modeling is a technique for organizing and extracting themes from large collections of unstructured text. Non-negative matrix factorization (NMF) is a common unsupervised approach that decomposes a term frequency-inverse document frequency (TF-
Externí odkaz:
http://arxiv.org/abs/2407.19616
Spectral methods provide highly accurate numerical solutions for partial differential equations, exhibiting exponential convergence with the number of spectral nodes. Traditionally, in addressing time-dependent nonlinear problems, attention has been
Externí odkaz:
http://arxiv.org/abs/2406.02505
Autor:
Danis, Mustafa Engin, Truong, Duc, Boureima, Ismael, Korobkin, Oleg, Rasmussen, Kim, Alexandrov, Boian
In this study, we introduce a tensor-train (TT) finite difference WENO method for solving compressible Euler equations. In a step-by-step manner, the tensorization of the governing equations is demonstrated. We also introduce \emph{LF-cross} and \emp
Externí odkaz:
http://arxiv.org/abs/2405.12301
Autor:
Barron, Ryan, Eren, Maksim E., Bhattarai, Manish, Wanna, Selma, Solovyev, Nicholas, Rasmussen, Kim, Alexandrov, Boian S., Nicholas, Charles, Matuszek, Cynthia
Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting action
Externí odkaz:
http://arxiv.org/abs/2403.16222
Autor:
Eren, Maksim E., Barron, Ryan, Bhattarai, Manish, Wanna, Selma, Solovyev, Nicholas, Rasmussen, Kim, Alexandrov, Boian S., Nicholas, Charles
National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods f
Externí odkaz:
http://arxiv.org/abs/2403.02546