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Learning by Asymmetric Parallel Boltzmann Machines

Bruno Apolloni, Diego de Falco
1991 Neural Computation  
A learning algorithm for Boltzmann machines. Cognitive Sci. 9, 147. 408 B. Apolloni and D. de Falco Apolloni, B., and de Falco, D. 1991a. Learning by parallel Boltzmann machines. IEEE Transact.  ...  Learning by feed-forward Boltzmann machines. Proceedings Neuronet 90, World Scientific, in press. Bertoni, A., Campadelli, P., and Grassani, F. 1989. Full parallelism in Boltzmann machines. Proc.  ... 
doi:10.1162/neco.1991.3.3.402 fatcat:cv6pluvhirfdrleezn7f4byh5y

Asymmetric Parallel Boltzmann Machines are Belief Networks

Radford M. Neal
1992 Neural Computation  
Communicated by Terrence J. Sejnowski Asymmetric Parallel Boltzmann Machines are Belief Networks Radford M.  ...  Learning by asymmetric parallel Boltzmann machines. Neural Comp. 3, 402-408. Neal, R. M. 1990. Learning stochastic feedforward networks. Tech. Rep.  ... 
doi:10.1162/neco.1992.4.6.832 fatcat:mfjzujcjqnchhpfwxzb6bcdxoe

Boltzmann Chains and Hidden Markov Models

Lawrence K. Saul, Michael I. Jordan
1994 Neural Information Processing Systems  
Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights.  ...  For these networks, we show how to implement the Boltzmann learning rule exactly, in polynomial time, without resort to simulated or mean-field annealing.  ...  This work was funded by ATR Human Information Processing Laboratories, Siemens Corporate Research, and NSF grant CDA-9404932 .  ... 
dblp:conf/nips/SaulJ94 fatcat:bkie4gabcvhpfmicepyghgi53m

Chaotic Boltzmann machines

Hideyuki Suzuki, Jun-ichi Imura, Yoshihiko Horio, Kazuyuki Aihara
2013 Scientific Reports  
The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine.  ...  Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines.  ...  Innovative R&D on Science and Technology (FIRST Program)'', initiated by the Council for Science and Technology Policy (CSTP).  ... 
doi:10.1038/srep01610 pmid:23558425 pmcid:PMC3617428 fatcat:p5r6eh7narcy3jnl2unmdbsmpq

Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning

Mahdi Nazm Bojnordi, Engin Ipek
2016 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA)  
The Boltzmann machine is a massively parallel computational model capable of solving a broad class of combinatorial optimization problems.  ...  This paper examines a new class of hardware accelerators for large-scale combinatorial optimization and deep learning based on memristive Boltzmann machines.  ...  This work was supported in part by NSF grant CCF-1533762.  ... 
doi:10.1109/hpca.2016.7446049 dblp:conf/hpca/BojnordiI16 fatcat:exms5os62rbvrm3qti6k74dieu

Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning

Mahdi Nazm Bojnordi, Engin Ipek
2017 2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S)  
The Boltzmann machine is a massively parallel computational model capable of solving a broad class of combinatorial optimization problems.  ...  This paper examines a new class of hardware accelerators for large-scale combinatorial optimization and deep learning based on memristive Boltzmann machines.  ...  This work was supported in part by NSF grant CCF-1533762.  ... 
doi:10.1109/e3s.2017.8246178 fatcat:7kybcqzpqnh4vkfx4xswy4qeuq

Learning in Boltzmann Trees

Lawrence Saul, Michael I. Jordan
1994 Neural Computation  
Finally, we discuss a number of possible extensions to our work. 2 Boltzmann Machines We briefly review the learning algorithm for the Boltzmann machine (Hertz et al. 1991).  ...  The learning rule, simple and locally based, lends itself to massive parallelism. The theory of Boltzmann learning, moreover, has a solid foundation in statistical mechanics.  ... 
doi:10.1162/neco.1994.6.6.1174 fatcat:f7edk6w54vcp5hrcot7olcd3rq

Page 1174 of Neural Computation Vol. 6, Issue 6 [page]

1994 Neural Computation  
The learning rule, simple and locally based, lends itself to massive parallelism. The theory of Boltzmann learning, moreover, has a solid foundation in statistical mechanics.  ...  Communicated by Radford M. Neal Learning in Boltzmann Trees Lawrence Saul Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139 USA Michael I.  ... 

An Overview of Hopfield Network and Boltzmann Machine

Saratha Sathasivam, Abdu Masanawa Sagir
2014 International journal of computational and electronics aspects in engineering  
This study gives an overview of Hopfield network and Boltzmann machine in terms of architectures, learning algorithms, comparison between these two networks from several different aspects as well as their  ...  The two well-known and commonly used types of recurrent neural networks, Hopfield neural network and Boltzmann machine have different structures and characteristics.  ...  The field goes by many names, such as connectionism; parallel distributed processing, neurocomputing, natural intelligent systems and machine learning algorithms.  ... 
doi:10.26706/ijceae.1.1.20141205 fatcat:ost6kvnxzjcqrdi4qa6oww3ybi

Page 25 of International Journal of Optical Computing Vol. 1, Issue 1 [page]

1990 International Journal of Optical Computing  
This model, known as the ‘Boltzmann machine’, is quite interesting because it offers a generalized approach to the exploitation of massive parallelism which is both supported by a rigorous mathematical  ...  However, a synchronous parallelism particularly suitable for implemen- tations has been identified® in the case of Boltzmann machines, opening the way to the design of specialized parallel hardware.  ... 

Higher-order Boltzmann machines

Terrence J. Sejnowski
1986 AIP Conference Proceedings  
T h e rate of learning for internal representations in a higher-order Boltzmann machine should be much faster t h a n for a second-order Boltzmann machine based on pairwise interactions.  ...  In a third-order Boltzmann machine, triples of units interact through symmetric conjunctive interactions. The Boltzmann learning algorithm is generalized t o higher-order interactions.  ...  T h e rate of learning for internal representations in a higher-order Boltzmann machine should be much faster t h a n for a second-order Boltzmann machine based on pairwise interactions.  ... 
doi:10.1063/1.36246 fatcat:dpfiseswozeyplef7oejoklkgu

Neural learning for distributions on categorical data

F.X. Albizuri, A.I. Gonzalez, M. Graña, A. d'Anjou
2000 Neurocomputing  
The distribution model is suggested by the log-linear models and it is a generalization of the binary Boltzmann machine.  ...  An example is provided to illustrate the modelling capability of the Boltzmann machine with discrete (non-binary) units.  ...  Acknowledgements This work was supported by the research Grants PI 1998/21 and UE 1999/1 from the Basque Government and MAT 99-1049-C03-03 from the CICYT.  ... 
doi:10.1016/s0925-2312(00)00291-5 fatcat:5j7h2j6orffmbmqokdmajprscm

Automated adjustment system of restricted Boltzmann machine

V. M. Sineglazov, O. R. Tofaniuk
2019 Electronics and Control Systems  
In this paper the problem of learning the deep believe neural network with help of a restricted Boltzmann machine and the choose of an optimal algorithm for its training is considered.  ...  To solve this problem, it is suggested to create an automated adjustment system of restricted Boltzmann machine, which choose the optimal training algorithm for this neural network.  ...  Restricted Boltzmann machine is adjusted according to the first, second and third algorithms one by one, for this training sample.  ... 
doi:10.18372/1990-5548.60.13814 fatcat:6zyvxc6oofcarlnd3tr3a24jai

Page 834 of Neural Computation Vol. 4, Issue 6 [page]

1992 Neural Computation  
Learning by asymmetric parallel Boltzmann machines. Neural Comp. 3, 402-408. Neal, R. M. 1990. Learning stochastic feedforward networks. Tech. Rep.  ...  The method is similar to Boltzmann machine learn- ing, but without the “negative phase.” Lack of a negative phase allows learning to proceed significantly faster than in a Boltzmann machine.  ... 

Quantum neuromorphic hardware for quantum artificial intelligence

Enrico Prati
2017 Journal of Physics, Conference Series  
The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields.  ...  Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing.  ...  machines, and by further resctricting the connection so that there are neither v-v nodes connections, nor h-h nodes connections -the so called restricted Boltzmann machine (RBM) -which in turn can be  ... 
doi:10.1088/1742-6596/880/1/012018 fatcat:ivfn67ojgngixgfwvwiq4eih2e
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