Zobrazeno 1 - 10
of 19
pro vyhledávání: '"EREN, MAKSIM E."'
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
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks, such as question-answering, sentiment analysis, text summarization, and machine translation. Howev
Externí odkaz:
http://arxiv.org/abs/2408.01008
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
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
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
Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomin
Externí odkaz:
http://arxiv.org/abs/2309.01350
Autor:
Eren, Maksim E., Solovyev, Nick, Bhattarai, Manish, Rasmussen, Kim, Nicholas, Charles, Alexandrov, Boian S.
As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular topic modeling approach is non-negative matrix factorization (NMF), a
Externí odkaz:
http://arxiv.org/abs/2208.09942