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pro vyhledávání: '"So, Yoonho"'
The effectiveness of large language models (LLMs) is not only measured by their ability to generate accurate outputs but also by their calibration-how well their confidence scores reflect the probability of their outputs being correct. While unsuperv
Externí odkaz:
http://arxiv.org/abs/2409.19817
Predicting and executing a sequence of actions without intermediate replanning, known as action chunking, is increasingly used in robot learning from human demonstrations. However, its effects on learned policies remain puzzling: some studies highlig
Externí odkaz:
http://arxiv.org/abs/2408.17355
Autor:
Gershon, Talia, Seelam, Seetharami, Belgodere, Brian, Bonilla, Milton, Hoang, Lan, Barnett, Danny, Chung, I-Hsin, Mohan, Apoorve, Chen, Ming-Hung, Luo, Lixiang, Walkup, Robert, Evangelinos, Constantinos, Salaria, Shweta, Dombrowa, Marc, Park, Yoonho, Kayi, Apo, Schour, Liran, Alim, Alim, Sydney, Ali, Maniotis, Pavlos, Schares, Laurent, Metzler, Bernard, Karacali-Akyamac, Bengi, Wen, Sophia, Chiba, Tatsuhiro, Choochotkaew, Sunyanan, Yoshimura, Takeshi, Misale, Claudia, Elengikal, Tonia, Connor, Kevin O, Liu, Zhuoran, Molina, Richard, Schneidenbach, Lars, Caden, James, Laibinis, Christopher, Fonseca, Carlos, Tarasov, Vasily, Sundararaman, Swaminathan, Schmuck, Frank, Guthridge, Scott, Cohn, Jeremy, Eshel, Marc, Muench, Paul, Liu, Runyu, Pointer, William, Wyskida, Drew, Krull, Bob, Rose, Ray, Wolfe, Brent, Cornejo, William, Walter, John, Malone, Colm, Perucci, Clifford, Franco, Frank, Hinds, Nigel, Calio, Bob, Druyan, Pavel, Kilduff, Robert, Kienle, John, McStay, Connor, Figueroa, Andrew, Connolly, Matthew, Fost, Edie, Roma, Gina, Fonseca, Jake, Levy, Ido, Payne, Michele, Schenkel, Ryan, Malki, Amir, Schneider, Lion, Narkhede, Aniruddha, Moshref, Shekeba, Kisin, Alexandra, Dodin, Olga, Rippon, Bill, Wrieth, Henry, Ganci, John, Colino, Johnny, Habeger-Rose, Donna, Pandey, Rakesh, Gidh, Aditya, Gaur, Aditya, Patterson, Dennis, Salmani, Samsuddin, Varma, Rambilas, Rumana, Rumana, Sharma, Shubham, Mishra, Mayank, Panda, Rameswar, Prasad, Aditya, Stallone, Matt, Zhang, Gaoyuan, Shen, Yikang, Cox, David, Puri, Ruchir, Agrawal, Dakshi, Thorstensen, Drew, Belog, Joel, Tang, Brent, Gupta, Saurabh Kumar, Biswas, Amitabha, Maheshwari, Anup, Gampel, Eran, Van Patten, Jason, Runion, Matthew, Kaki, Sai, Bogin, Yigal, Reitz, Brian, Pritko, Steve, Najam, Shahan, Nambala, Surya, Chirra, Radhika, Welp, Rick, DiMitri, Frank, Telles, Felipe, Arvelo, Amilcar, Chu, King, Seminaro, Ed, Schram, Andrew, Eickhoff, Felix, Hanson, William, Mckeever, Eric, Joseph, Dinakaran, Chaudhary, Piyush, Shivam, Piyush, Chaudhary, Puneet, Jones, Wesley, Guthrie, Robert, Bostic, Chris, Islam, Rezaul, Duersch, Steve, Sawdon, Wayne, Lewars, John, Klos, Matthew, Spriggs, Michael, McMillan, Bill, Gao, George, Kamra, Ashish, Singh, Gaurav, Curry, Marc, Katarki, Tushar, Talerico, Joe, Shi, Zenghui, Malleni, Sai Sindhur, Gallen, Erwan
AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational
Externí odkaz:
http://arxiv.org/abs/2407.05467
Temporal Graph Neural Networks (TGNN) have the ability to capture both the graph topology and dynamic dependencies of interactions within a graph over time. There has been a growing need to explain the predictions of TGNN models due to the difficulty
Externí odkaz:
http://arxiv.org/abs/2406.13214
Mitigating hallucination issues is a key challenge that must be overcome to reliably deploy large language models (LLMs) in real-world scenarios. Recently, various methods have been proposed to detect and revise factual errors in LLM-generated texts,
Externí odkaz:
http://arxiv.org/abs/2402.17097
Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervised learning to new modalities is non-trivial because the specifics of
Externí odkaz:
http://arxiv.org/abs/2402.14789
The standard way to teach models is by feeding them lots of data. However, this approach often teaches models incorrect ideas because they pick up on misleading signals in the data. To prevent such misconceptions, we must necessarily provide addition
Externí odkaz:
http://arxiv.org/abs/2402.03715
Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performance under distribution shifts. Current approaches to robust fine-tuning
Externí odkaz:
http://arxiv.org/abs/2401.10220
Autor:
Kim, Peter Yongho, Kwon, Junbeom, Joo, Sunghwan, Bae, Sangyoon, Lee, Donggyu, Jung, Yoonho, Yoo, Shinjae, Cha, Jiook, Moon, Taesup
Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of fea
Externí odkaz:
http://arxiv.org/abs/2307.05916
Effective machine learning models learn both robust features that directly determine the outcome of interest (e.g., an object with wheels is more likely to be a car), and shortcut features (e.g., an object on a road is more likely to be a car). The l
Externí odkaz:
http://arxiv.org/abs/2306.11120