Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Alexandrov, Boian S."'
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:
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
Autor:
Barron, Ryan, Eren, Maksim E., Bhattarai, Manish, Boureima, Ismael, Matuszek, Cynthia, Alexandrov, Boian S.
In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components
Externí odkaz:
http://arxiv.org/abs/2407.19125
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:
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
Emerging tensor network techniques for solutions of Partial Differential Equations (PDEs), known for their ability to break the curse of dimensionality, deliver new mathematical methods for ultrafast numerical solutions of high-dimensional problems.
Externí odkaz:
http://arxiv.org/abs/2402.18073
Autor:
Solovyev, Nicholas, Barron, Ryan, Bhattarai, Manish, Eren, Maksim E., Rasmussen, Kim O., Alexandrov, Boian S.
Highly specific datasets of scientific literature are important for both research and education. However, it is difficult to build such datasets at scale. A common approach is to build these datasets reductively by applying topic modeling on an estab
Externí odkaz:
http://arxiv.org/abs/2309.10772
Autor:
Eren, Maksim E., Bhattarai, Manish, Joyce, Robert J., Raff, Edward, Nicholas, Charles, Alexandrov, Boian S.
Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of r
Externí odkaz:
http://arxiv.org/abs/2309.06643