Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Tavares, Zenna."'
Autor:
Mineault, Patrick, Zanichelli, Niccolò, Peng, Joanne Zichen, Arkhipov, Anton, Bingham, Eli, Jara-Ettinger, Julian, Mackevicius, Emily, Marblestone, Adam, Mattar, Marcelo, Payne, Andrew, Sanborn, Sophia, Schroeder, Karen, Tavares, Zenna, Tolias, Andreas
As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general intelligence, they perform robustly even under conditions that deviat
Externí odkaz:
http://arxiv.org/abs/2411.18526
Autor:
Li, Wen-Ding, Hu, Keya, Larsen, Carter, Wu, Yuqing, Alford, Simon, Woo, Caleb, Dunn, Spencer M., Tang, Hao, Naim, Michelangelo, Nguyen, Dat, Zheng, Wei-Long, Tavares, Zenna, Pu, Yewen, Ellis, Kevin
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC b
Externí odkaz:
http://arxiv.org/abs/2411.02272
Autor:
Peters, Benjamin, DiCarlo, James J., Gureckis, Todd, Haefner, Ralf, Isik, Leyla, Tenenbaum, Joshua, Konkle, Talia, Naselaris, Thomas, Stachenfeld, Kimberly, Tavares, Zenna, Tsao, Doris, Yildirim, Ilker, Kriegeskorte, Nikolaus
Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-u
Externí odkaz:
http://arxiv.org/abs/2401.06005
Autor:
Berke, Marlene D., Azerbayev, Zhangir, Belledonne, Mario, Tavares, Zenna, Jara-Ettinger, Julian
Humans have the capacity to question what we see and to recognize when our vision is unreliable (e.g., when we realize that we are experiencing a visual illusion). Inspired by this capacity, we present MetaCOG: a hierarchical probabilistic model that
Externí odkaz:
http://arxiv.org/abs/2110.03105
Autor:
Tavares, Zenna.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February, 2020
Manuscript.
Includes bibliographical references (pages 139-149).
Human reasoning is complex, messy, and approximate, and
Manuscript.
Includes bibliographical references (pages 139-149).
Human reasoning is complex, messy, and approximate, and
Externí odkaz:
https://hdl.handle.net/1721.1/138517
Autor:
Tavares, Zenna, Zhang, Xin, Minaysan, Edgar, Burroni, Javier, Ranganath, Rajesh, Lezama, Armando Solar
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At face value however, distributional properties are not random variable
Externí odkaz:
http://arxiv.org/abs/1903.10556
We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate. A predicate is a Boolean valued function which expresses a yes/no question about a domain. Our contribution, which we call predicate exchange, con
Externí odkaz:
http://arxiv.org/abs/1901.05437
Autor:
Peters B; Zuckerman Mind Brain Behavior Institute, Columbia University.; School of Psychology & Neuroscience, University of Glasgow., DiCarlo JJ; Department of Brain and Cognitive Sciences, MIT.; McGovern Institute for Brain Research, MIT.; NSF Center for Brains, Minds and Machines, MIT.; Quest for Intelligence, Schwarzman College of Computing, MIT., Gureckis T; Department of Psychology, New York University., Haefner R; Brain and Cognitive Sciences, University of Rochester.; Center for Visual Science, University of Rochester., Isik L; Department of Cognitive Science, Johns Hopkins University., Tenenbaum J; Department of Brain and Cognitive Sciences, MIT.; NSF Center for Brains, Minds and Machines, MIT.; Computer Science and Artificial Intelligence Laboratory, MIT., Konkle T; Department of Psychology, Harvard University.; Center for Brain Science, Harvard University.; Kempner Institute for Natural and Artificial Intelligence, Harvard University., Naselaris T; Department of Neuroscience, University of Minnesota., Stachenfeld K; DeepMind., Tavares Z; Zuckerman Mind Brain Behavior Institute, Columbia University.; Data Science Institute, Columbia University., Tsao D; Dept of Molecular & Cell Biology, University of California Berkeley.; Howard Hughes Medical Institute., Yildirim I; Department of Psychology, Yale University.; Department of Statistics and Data Science, Yale University., Kriegeskorte N; Zuckerman Mind Brain Behavior Institute, Columbia University.; Department of Psychology, Columbia University.; Department of Neuroscience, Columbia University.; Department of Electrical Engineering, Columbia University.
Publikováno v:
ArXiv [ArXiv] 2024 Jan 11. Date of Electronic Publication: 2024 Jan 11.
