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pro vyhledávání: '"BROWN, P B"'
Autor:
Brown, Samuel B., Young, Stephen, Wagenknecht, Adam, Jakubisin, Daniel, Thornton, Charles E., Orndorff, Aaron, Headley, William C.
Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this c
Externí odkaz:
http://arxiv.org/abs/2410.03423
Autor:
Pion-Tonachini, Luca, Bouchard, Kristofer, Martin, Hector Garcia, Peisert, Sean, Holtz, W. Bradley, Aswani, Anil, Dwivedi, Dipankar, Wainwright, Haruko, Pilania, Ghanshyam, Nachman, Benjamin, Marrone, Babetta L., Falco, Nicola, Prabhat, Arnold, Daniel, Wolf-Yadlin, Alejandro, Powers, Sarah, Climer, Sharlee, Jackson, Quinn, Carlson, Ty, Sohn, Michael, Zwart, Petrus, Kumar, Neeraj, Justice, Amy, Tomlin, Claire, Jacobson, Daniel, Micklem, Gos, Gkoutos, Georgios V., Bickel, Peter J., Cazier, Jean-Baptiste, Müller, Juliane, Webb-Robertson, Bobbie-Jo, Stevens, Rick, Anderson, Mark, Kreutz-Delgado, Ken, Mahoney, Michael W., Brown, James B.
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering pa
Externí odkaz:
http://arxiv.org/abs/2111.13786
Autor:
Wu, Yulun, Cashman, Mikaela, Choma, Nicholas, Prates, Érica T., Vergara, Verónica G. Melesse, Shah, Manesh, Chen, Andrew, Clyde, Austin, Brettin, Thomas S., de Jong, Wibe A., Kumar, Neeraj, Head, Martha S., Stevens, Rick L., Nugent, Peter, Jacobson, Daniel A., Brown, James B.
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
Externí odkaz:
http://arxiv.org/abs/2106.02190
Autor:
Henighan, Tom, Kaplan, Jared, Katz, Mor, Chen, Mark, Hesse, Christopher, Jackson, Jacob, Jun, Heewoo, Brown, Tom B., Dhariwal, Prafulla, Gray, Scott, Hallacy, Chris, Mann, Benjamin, Radford, Alec, Ramesh, Aditya, Ryder, Nick, Ziegler, Daniel M., Schulman, John, Amodei, Dario, McCandlish, Sam
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothl
Externí odkaz:
http://arxiv.org/abs/2010.14701
Autor:
Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal, Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh, Aditya, Ziegler, Daniel M., Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark, Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher, McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-speci
Externí odkaz:
http://arxiv.org/abs/2005.14165
Autor:
Hernandez, Danny, Brown, Tom B.
Three factors drive the advance of AI: algorithmic innovation, data, and the amount of compute available for training. Algorithmic progress has traditionally been more difficult to quantify than compute and data. In this work, we argue that algorithm
Externí odkaz:
http://arxiv.org/abs/2005.04305
Autor:
Kaplan, Jared, McCandlish, Sam, Henighan, Tom, Brown, Tom B., Chess, Benjamin, Child, Rewon, Gray, Scott, Radford, Alec, Wu, Jeffrey, Amodei, Dario
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of m
Externí odkaz:
http://arxiv.org/abs/2001.08361
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