Zobrazeno 1 - 10
of 1 875
pro vyhledávání: '"Botvinick, A"'
Autor:
Goldberg, Beth, Acosta-Navas, Diana, Bakker, Michiel, Beacock, Ian, Botvinick, Matt, Buch, Prateek, DiResta, Renée, Donthi, Nandika, Fast, Nathanael, Iyer, Ravi, Jalan, Zaria, Konya, Andrew, Danciu, Grace Kwak, Landemore, Hélène, Marwick, Alice, Miller, Carl, Ovadya, Aviv, Saltz, Emily, Schirch, Lisa, Shalom, Dalit, Siddarth, Divya, Sieker, Felix, Small, Christopher, Stray, Jonathan, Tang, Audrey, Tessler, Michael Henry, Zhang, Amy
Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift tow
Externí odkaz:
http://arxiv.org/abs/2412.09988
Invariant measures are widely used to compare chaotic dynamical systems, as they offer robustness to noisy data, uncertain initial conditions, and irregular sampling. However, large classes of systems with distinct transient dynamics can still exhibi
Externí odkaz:
http://arxiv.org/abs/2412.00589
The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that
Externí odkaz:
http://arxiv.org/abs/2409.08768
Autor:
Summerfield, Christopher, Argyle, Lisa, Bakker, Michiel, Collins, Teddy, Durmus, Esin, Eloundou, Tyna, Gabriel, Iason, Ganguli, Deep, Hackenburg, Kobi, Hadfield, Gillian, Hewitt, Luke, Huang, Saffron, Landemore, Helene, Marchal, Nahema, Ovadya, Aviv, Procaccia, Ariel, Risse, Mathias, Schneier, Bruce, Seger, Elizabeth, Siddarth, Divya, Sætra, Henrik Skaug, Tessler, MH, Botvinick, Matthew
Advanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of
Externí odkaz:
http://arxiv.org/abs/2409.06729
Autor:
Koster, Raphael, Pîslar, Miruna, Tacchetti, Andrea, Balaguer, Jan, Liu, Leqi, Elie, Romuald, Hauser, Oliver P., Tuyls, Karl, Botvinick, Matt, Summerfield, Christopher
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciproc
Externí odkaz:
http://arxiv.org/abs/2404.15059
Autor:
Binz, Marcel, Alaniz, Stephan, Roskies, Adina, Aczel, Balazs, Bergstrom, Carl T., Allen, Colin, Schad, Daniel, Wulff, Dirk, West, Jevin D., Zhang, Qiong, Shiffrin, Richard M., Gershman, Samuel J., Popov, Ven, Bender, Emily M., Marelli, Marco, Botvinick, Matthew M., Akata, Zeynep, Schulz, Eric
Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advent of large language models affect the practice of science? For t
Externí odkaz:
http://arxiv.org/abs/2312.03759
Autor:
Coda-Forno, Julian, Binz, Marcel, Akata, Zeynep, Botvinick, Matthew, Wang, Jane X., Schulz, Eric
Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonstrations -- is seen as one of the main contributors to their success. I
Externí odkaz:
http://arxiv.org/abs/2305.12907
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science 33, 103108 (2023)
Motivated by the computational difficulties incurred by popular deep learning algorithms for the generative modeling of temporal densities, we propose a cheap alternative which requires minimal hyperparameter tuning and scales favorably to high dimen
Externí odkaz:
http://arxiv.org/abs/2304.09663
Autor:
Binz, Marcel, Dasgupta, Ishita, Jagadish, Akshay, Botvinick, Matthew, Wang, Jane X., Schulz, Eric
Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand. In recent years, this framework has established itself as a promising tool for building models of hu
Externí odkaz:
http://arxiv.org/abs/2304.06729
Autor:
Ke, Nan Rosemary, Dunn, Sara-Jane, Bornschein, Jorg, Chiappa, Silvia, Rey, Melanie, Lespiau, Jean-Baptiste, Cassirer, Albin, Wang, Jane, Weber, Theophane, Barrett, David, Botvinick, Matthew, Goyal, Anirudh, Mozer, Mike, Rezende, Danilo
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational d
Externí odkaz:
http://arxiv.org/abs/2304.05823