Results 41 to 50 of about 288 (66)

Entropy of random symbolic high-order bilinear Markov chains

, 2017
The main goal of this paper is to develop an estimate for the entropy of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet.
Melnik, S. S., Usatenko, O. V.
core   +1 more source

Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors

, 2009
In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system.
A. C. R. Martins, J. B. Batista, J. Lorenz, L. da F. Costa, M. Purvis, P. S. Sajja, R. Hegselmann, V. Botti   +7 more
core   +1 more source

Synchronizations in small-world networks of spiking neurons: Diffusive versus sigmoid couplings

, 2005
By using a semi-analytical dynamical mean-field approximation previously proposed by the author [H. Hasegawa, Phys. Rev. E, {\bf 70}, 066107 (2004)], we have studied the synchronization of stochastic, small-world (SW) networks of FitzHugh-Nagumo neurons ...
A. Barabási, Hideo Hasegawa, J. R. Gibson   +2 more
core   +1 more source

Thouless-Anderson-Palmer equation for analog neural network with temporally fluctuating white synaptic noise

, 2007
Effects of synaptic noise on the retrieval process of associative memory neural networks are studied from the viewpoint of neurobiological and biophysical understanding of information processing in the brain.
Akihisa Ichiki, Amari S, Anderson C R, Benzi R, Choi M Y, Frank T D, Gardiner C W, Garrido P L, Ichiki A Shiino M, Kuhn R, Marro J, Masatoshi Shiino, Mézard M, Nicolis C, Shiino M, Shiino M, Torres J J, Treves A, Uezu T   +18 more
core   +1 more source

Quantum pattern recognition with liquid-state nuclear magnetic resonance

, 2009
A novel quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian.
A. Messiah, D. Aharonov, Jorge L. Neves, L. M. K. Vandersypen, N. D. Mermin, R. R. Ernst, Rodion Neigovzen, Rudolf Sollacher, S. Haykin, Steffen J. Glaser, U. Haeberlen   +10 more
core   +1 more source

Multitasking associative networks

, 2012
We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious ...
Adriano Barra, Andrea Galluzzi, D. J. Amit, Elena Agliari, Francesco Guerra, Francesco Moauro, K. Hwang, M. Mézard, M. F. Bear   +8 more
core   +1 more source

Variability in mental health reporting among refugees and migrants in need of protection: new evidence from a weekly panel survey. [PDF]

BMC Public Health, 2023
Weitzman A, Blanton M, Camacho GB.
europepmc   +1 more source

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach. [PDF]

Sensors (Basel), 2021
Morales MD, Antelis JM, Moreno C, Nesterov AI.   +3 more
europepmc   +1 more source

On-Line Learning Theory of Soft Committee Machines with Correlated Hidden Units - Steepest Gradient Descent and Natural Gradient Descent -

, 2002
The permutation symmetry of the hidden units in multilayer perceptrons causes the saddle structure and plateaus of the learning dynamics in gradient learning methods.
Amari S., Amari S., Fukumizu K., Rattray M., Saad D., Yang H. H.   +5 more
core   +1 more source

Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments

, 2008
A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNN ...
Apollonio, Beale, Creutz, Duane, Kaien Zhu, Mackenzie, Neal, Wei Xu, Weiwei Xu, Ye Xu, Yixiong Meng   +10 more
core   +1 more source