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In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Language Models (LLMs) in generating high-quality scientific syntheses. This
Externí odkaz:
http://arxiv.org/abs/2409.18812
Autor:
Kashyap, Satyananda, D'Souza, Niharika S., Shi, Luyao, Wong, Ken C. L., Wang, Hongzhi, Syeda-Mahmood, Tanveer
Content-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-scale content storage faces chal
Externí odkaz:
http://arxiv.org/abs/2409.16408
Autor:
Putterman, Harald, Noh, Kyungjoo, Hann, Connor T., MacCabe, Gregory S., Aghaeimeibodi, Shahriar, Patel, Rishi N., Lee, Menyoung, Jones, William M., Moradinejad, Hesam, Rodriguez, Roberto, Mahuli, Neha, Rose, Jefferson, Owens, John Clai, Levine, Harry, Rosenfeld, Emma, Reinhold, Philip, Moncelsi, Lorenzo, Alcid, Joshua Ari, Alidoust, Nasser, Arrangoiz-Arriola, Patricio, Barnett, James, Bienias, Przemyslaw, Carson, Hugh A., Chen, Cliff, Chen, Li, Chinkezian, Harutiun, Chisholm, Eric M., Chou, Ming-Han, Clerk, Aashish, Clifford, Andrew, Cosmic, R., Curiel, Ana Valdes, Davis, Erik, DeLorenzo, Laura, D'Ewart, J. Mitchell, Diky, Art, D'Souza, Nathan, Dumitrescu, Philipp T., Eisenmann, Shmuel, Elkhouly, Essam, Evenbly, Glen, Fang, Michael T., Fang, Yawen, Fling, Matthew J., Fon, Warren, Garcia, Gabriel, Gorshkov, Alexey V., Grant, Julia A., Gray, Mason J., Grimberg, Sebastian, Grimsmo, Arne L., Haim, Arbel, Hand, Justin, He, Yuan, Hernandez, Mike, Hover, David, Hung, Jimmy S. C., Hunt, Matthew, Iverson, Joe, Jarrige, Ignace, Jaskula, Jean-Christophe, Jiang, Liang, Kalaee, Mahmoud, Karabalin, Rassul, Karalekas, Peter J., Keller, Andrew J., Khalajhedayati, Amirhossein, Kubica, Aleksander, Lee, Hanho, Leroux, Catherine, Lieu, Simon, Ly, Victor, Madrigal, Keven Villegas, Marcaud, Guillaume, McCabe, Gavin, Miles, Cody, Milsted, Ashley, Minguzzi, Joaquin, Mishra, Anurag, Mukherjee, Biswaroop, Naghiloo, Mahdi, Oblepias, Eric, Ortuno, Gerson, Pagdilao, Jason, Pancotti, Nicola, Panduro, Ashley, Paquette, JP, Park, Minje, Peairs, Gregory A., Perello, David, Peterson, Eric C., Ponte, Sophia, Preskill, John, Qiao, Johnson, Refael, Gil, Resnick, Rachel, Retzker, Alex, Reyna, Omar A., Runyan, Marc, Ryan, Colm A., Sahmoud, Abdulrahman, Sanchez, Ernesto, Sanil, Rohan, Sankar, Krishanu, Sato, Yuki, Scaffidi, Thomas, Siavoshi, Salome, Sivarajah, Prasahnt, Skogland, Trenton, Su, Chun-Ju, Swenson, Loren J., Teo, Stephanie M., Tomada, Astrid, Torlai, Giacomo, Wollack, E. Alex, Ye, Yufeng, Zerrudo, Jessica A., Zhang, Kailing, Brandão, Fernando G. S. L., Matheny, Matthew H., Painter, Oskar
In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically as
Externí odkaz:
http://arxiv.org/abs/2409.13025
This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to explore the p
Externí odkaz:
http://arxiv.org/abs/2409.10146
Autor:
Chen, Sheng, Tang, Zihao, Cabezas, Mariano, Wang, Xinyi, D'Souza, Arkiev, Barnett, Michael, Calamante, Fernando, Cai, Weidong, Wang, Chenyu
Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules, offering the capability to inspect tissue microstructures and is the only in-vivo method to reconstruct white m
Externí odkaz:
http://arxiv.org/abs/2409.07186
The increasing amount of published scholarly articles, exceeding 2.5 million yearly, raises the challenge for researchers in following scientific progress. Integrating the contributions from scholarly articles into a novel type of cognitive knowledge
Externí odkaz:
http://arxiv.org/abs/2409.06433
Autor:
Anselmi, Gianluca, Vekaria, Yash, D'Souza, Alexander, Callejo, Patricia, Mandalari, Anna Maria, Shafiq, Zubair
Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matc
Externí odkaz:
http://arxiv.org/abs/2409.06203
The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate data has been shown to lead to model collapse and invite propagation of biases. These limi
Externí odkaz:
http://arxiv.org/abs/2408.14960
Autor:
Kabongo, Salomon, D'Souza, Jennifer
This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Task, Dataset, Metric, Score) quadruples from articles. It aims to stream
Externí odkaz:
http://arxiv.org/abs/2408.10141
Autor:
Chen, Yiming, D'Souza, Niharika S., Mandepally, Akshith, Henninger, Patrick, Kashyap, Satyananda, Karani, Neerav, Dey, Neel, Zachary, Marcos, Rizq, Raed, Chouinard, Paul, Golland, Polina, Syeda-Mahmood, Tanveer F.
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical
Externí odkaz:
http://arxiv.org/abs/2408.04826