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  1. Energy-based models are an attractive option for generative unsupervised [1, 2] and supervised learning [].Among them, two closely related models, classical Hopfield networks [] and Boltzmann machines (BMs) [], have played a paradigmatic role in the development of the field.The first serve as associative memory models, allowing for the storage and dynamical retrieval of system configurations ...
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  3. Oct 22, 2023It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones. However, sampling -- and thus training -- these models can be numerically hard. Recently we proposed a regularisation of the connections of Boltzmann machines, in order to control the energy landscape of the model, paving ...
  4. cs.unibocconi.eu

    Universal representation by Boltzmann machines with Regularised Axons, arXiv:2310.14395 Maciej Lewenstein Head of the Quantum Optics Theory group ICFO Institute of Barcelona 05 DECEMBER 2023 12:00 PM Bocconi University Room 3-E4-SR03 Via Röntgen 1, Milano (3° floor)
  5. Universal representation by Boltzmann machines withRegularised Axons Przemysla w R. Grzybowski,1,2,∗ Antoni Jankiewicz,2 Eloy Pin˜ol,1 David Cirauqui,1,3 Dorota H. Grzybowska, Pawel M. Petrykowski, Miguel Angel Garc´ıa-March,´ 4 Maciej Lewenstein,1,5 Gorka Mun˜oz-Gil,6 and Alejandro Pozas-Kerstjens7,8,9 1ICFO - Institut de Ci`encies Fot ...
  6. semanticscholar.org

    Oct 22, 2023It is formally proved that regularised Boltzmann machines preserve the ability to represent arbitrary distributions, in conjunction with controlling the number of energy local minima, thus enabling easy sampling and training. It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ...
  7. paperswithcode.com

    Oct 22, 2023Universal representation by Boltzmann machines with Regularised Axons ... It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones. ... of energy local minima, thus enabling easy \emph{guided} sampling and training. Furthermore ...
  8. export.arxiv.org

    Oct 22, 2023Abstract: It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones. However, sampling -- and thus training -- these models can be numerically hard. Recently we proposed a regularisation of the connections of Boltzmann machines, in order to control the energy landscape of the ...
  9. cs.mcgill.ca

    18.2 Learning in a Boltzmann Machine Learning in a Boltzmann machine involves optimizing the weight values w i,j in order to achieve a desired equilibrium distribution. In particular, our goal is to model the distribution P(x) of some binary-valued feature vector x 2 {0,1}m. However, in order to understand how this learning proceeds, we first
  10. The project. Launched in April 2023, PASQuanS2 sets out to transform the development of programmable quantum simulation in Europe over the next seven years.Following a two-stage approach, one of the major objectives of this first project phase PASQuanS2.1 (running for the next 3.5 years), is the development of quantum simulators with at least 2,000 atoms and a path towards 10,000 while ...

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