Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Vértes, Eszter"'
Autor:
Walker, Jacob, Vértes, Eszter, Li, Yazhe, Dulac-Arnold, Gabriel, Anand, Ankesh, Weber, Théophane, Hamrick, Jessica B.
State of the art reinforcement learning has enabled training agents on tasks of ever increasing complexity. However, the current paradigm tends to favor training agents from scratch on every new task or on collections of tasks with a view towards gen
Externí odkaz:
http://arxiv.org/abs/2302.04009
Autor:
Kossen, Jannik, Cangea, Cătălina, Vértes, Eszter, Jaegle, Andrew, Patraucean, Viorica, Ktena, Ira, Tomasev, Nenad, Belgrave, Danielle
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily available at test time and must instead be acquired at significant cost.
Externí odkaz:
http://arxiv.org/abs/2211.05039
Autor:
Filos, Angelos, Vértes, Eszter, Marinho, Zita, Farquhar, Gregory, Borsa, Diana, Friesen, Abram, Behbahani, Feryal, Schaul, Tom, Barreto, André, Osindero, Simon
Using a model of the environment and a value function, an agent can construct many estimates of a state's value, by unrolling the model for different lengths and bootstrapping with its value function. Our key insight is that one can treat this set of
Externí odkaz:
http://arxiv.org/abs/2112.04153
Autor:
Anand, Ankesh, Walker, Jacob, Li, Yazhe, Vértes, Eszter, Schrittwieser, Julian, Ozair, Sherjil, Weber, Théophane, Hamrick, Jessica B.
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well
Externí odkaz:
http://arxiv.org/abs/2111.01587
Autor:
Vertes, Eszter, Sahani, Maneesh
Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are o
Externí odkaz:
http://arxiv.org/abs/1906.09480
Autor:
Vertes, Eszter, Sahani, Maneesh
We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the origina
Externí odkaz:
http://arxiv.org/abs/1805.11051
Autor:
Ecker, András, Bagi, Bence, Vértes, Eszter, Steinbach-Németh, Orsolya, Karlócai, Rita, Miklós, István, Hájos, Norbert, Freund, Tamás, Gulyás, Attila, Káli, Szabolcs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2764::12c439b0d5da998bcc9e62eef573db60
https://eprints.sztaki.hu/10329/
https://eprints.sztaki.hu/10329/
Autor:
Káli, Szabolcs1,2 kali@koki.hu, Vértes, Eszter1,2,3, Nagy, Dávid G.1, Freund, Tamás F.1,2, Gulyás, Attila I.1
Publikováno v:
BMC Neuroscience. 2013, Vol. 14 Issue Suppl 1, p1-2. 2p.
Autor:
Ecker A; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary., Bagi B; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary., Vértes E; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary., Steinbach-Németh O; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary., Karlócai MR; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary., Papp OI; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary., Miklós I; Alfréd Rényi Institute of Mathematics, Eötvös Loránd Research Network, Budapest, Hungary.; Institute for Computer Science and Control, Eötvös Loránd Research Network, Budapest, Hungary., Hájos N; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary., Freund TF; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary., Gulyás AI; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary., Káli S; Institute of Experimental Medicine, Eötvös Loránd Research Network, Budapest, Hungary.; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
Publikováno v:
ELife [Elife] 2022 Jan 18; Vol. 11. Date of Electronic Publication: 2022 Jan 18.