Zobrazeno 1 - 10
of 30 196
pro vyhledávání: '"Parr, A."'
Autor:
Whyte, Christopher J., Corcoran, Andrew W., Robinson, Jonathan, Smith, Ryan, Moran, Rosalyn J., Parr, Thomas, Friston, Karl J., Seth, Anil K., Hohwy, Jakob
The multifaceted nature of experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theo
Externí odkaz:
http://arxiv.org/abs/2410.06633
Autor:
Walsh, James, Gass, Daniel G., Pollan, Raul Ramos, Wright, Paul J., Galvez, Richard, Kasmanoff, Noah, Naradowsky, Jason, Spalding, Anne, Parr, James, Baydin, Atılım Güneş
SDO-FM is a foundation model using data from NASA's Solar Dynamics Observatory (SDO) spacecraft; integrating three separate instruments to encapsulate the Sun's complex physical interactions into a multi-modal embedding space. This model can be used
Externí odkaz:
http://arxiv.org/abs/2410.02530
Autor:
Da Costa, Lancelot, Da Costa, Nathaël, Heins, Conor, Medrano, Johan, Pavliotis, Grigorios A., Parr, Thomas, Meera, Ajith Anil, Friston, Karl
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some non-trivial temporal correlation structure, which renders the SDE non-
Externí odkaz:
http://arxiv.org/abs/2409.15532
Autor:
Friston, Karl, Heins, Conor, Verbelen, Tim, Da Costa, Lancelot, Salvatori, Tommaso, Markovic, Dimitrije, Tschantz, Alexander, Koudahl, Magnus, Buckley, Christopher, Parr, Thomas
This paper describes a discrete state-space model -- and accompanying methods -- for generative modelling. This model generalises partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active infe
Externí odkaz:
http://arxiv.org/abs/2407.20292
Autor:
Allen, Cameron, Kirtland, Aaron, Tao, Ruo Yu, Lobel, Sam, Scott, Daniel, Petrocelli, Nicholas, Gottesman, Omer, Parr, Ronald, Littman, Michael L., Konidaris, George
Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can a
Externí odkaz:
http://arxiv.org/abs/2407.07333
Autor:
Rudin, Cynthia, Zhong, Chudi, Semenova, Lesia, Seltzer, Margo, Parr, Ronald, Liu, Jiachang, Katta, Srikar, Donnelly, Jon, Chen, Harry, Boner, Zachery
Publikováno v:
ICML (spotlight), 2024
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, b
Externí odkaz:
http://arxiv.org/abs/2407.04846
Autor:
Lobel, Sam, Parr, Ronald
We present a bound for value-prediction error with respect to model misspecification that is tight, including constant factors. This is a direct improvement of the "simulation lemma," a foundational result in reinforcement learning. We demonstrate th
Externí odkaz:
http://arxiv.org/abs/2406.16249
Autor:
Fang, Fang, Wang, Kenneth, Liu, Vincent S., Wang, Yu, Cimmino, Ryan, Wei, Julia, Bintz, Marcus, Parr, Avery, Kemp, Jack, Ni, Kang-Kuen, Yao, Norman Y.
At continuous phase transitions, quantum many-body systems exhibit scale-invariance and complex, emergent universal behavior. Most strikingly, at a quantum critical point, correlations decay as a power law, with exponents determined by a set of unive
Externí odkaz:
http://arxiv.org/abs/2402.15376
Autor:
Friston, Karl J., Salvatori, Tommaso, Isomura, Takuya, Tschantz, Alexander, Kiefer, Alex, Verbelen, Tim, Koudahl, Magnus, Paul, Aswin, Parr, Thomas, Razi, Adeel, Kagan, Brett, Buckley, Christopher L., Ramstead, Maxwell J. D.
Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the
Externí odkaz:
http://arxiv.org/abs/2312.07547
Autor:
Friston, Karl J., Da Costa, Lancelot, Tschantz, Alexander, Kiefer, Alex, Salvatori, Tommaso, Neacsu, Victorita, Koudahl, Magnus, Heins, Conor, Sajid, Noor, Markovic, Dimitrije, Parr, Thomas, Verbelen, Tim, Buckley, Christopher L
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move -
Externí odkaz:
http://arxiv.org/abs/2311.10300