Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Barron, Ryan"'
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:
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:
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:
Bhattarai, Manish, Kaymak, Mehmet Cagri, Barron, Ryan, Nebgen, Ben, Rasmussen, Kim, Alexandrov, Boian
As machine learning techniques become increasingly prevalent in data analysis, the threat of adversarial attacks has surged, necessitating robust defense mechanisms. Among these defenses, methods exploiting low-rank approximations for input data prep
Externí odkaz:
http://arxiv.org/abs/2309.01077
Virtual, Augmented, and Mixed-Reality for Human-Robot Interactions 2023 (VAM-HRI ’23), Stockholm, Sweden, March 2023
In this paper, we present a shared manipulation task performed both in virtual reality with a simulated robot and in the real
In this paper, we present a shared manipulation task performed both in virtual reality with a simulated robot and in the real
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f75e501740f5fdf9a6ec8aa2791f6d4