24 papers · ranked by Valyu relevance
Giacomo Torlai, Roger G. Melko
A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we develop a Boltzmann machine that is capable of modelling thermodynamic observables for physical systems in thermal equilibrium. Through…
Lorenzo Rosset, Roberto Netti, Anna Paola Muntoni, Martin Weigt + 1 more
In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package adabmDCA 2.0 is available in different programming languages (C++, Julia, Python) usable on different architectures…
Siddhartha Srivastava, Veera Sundararaghavan
A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. BM are undirected graphs with a network of visible and hidden nodes where the former is used as the reading site. In contrast, the latter is used to manipulate visible states’ probability. In…
Jinmeng Song, Chun Yuan
—We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Calo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective…
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the…
Takayuki Osogami
We review Boltzmann machines and energy-based models. A Boltzmann machine defines a probability distribution over binary-valued patterns. One can learn parameters of a Boltzmann machine via gradient based approaches in a way that log likelihood of data is increased. The gradient and Hessian of a Boltzmann machine admit…
Paul J N Brodersen, Colin J Akerman
In the search for biologically plausible but mathematically precise theories of learning in the brain, recent studies have begun to investigate how key assumptions underlying algorithms for supervised learning in artificial neural networks can be relaxed in biologically plausible ways. Turning to unsupervised learning…
Takehito Sato, Masayuki Ohzeki, Kazuyuki Tanaka
Quantum annealing was originally proposed as an approach for solving combinatorial optimization problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and various environmental factors in the hardware hamper the determination of optimal…
Takayuki Osogami, Makoto Otsuka
plasticity Authors: ['Takayuki Osogami' 'Makoto Otsuka'] We propose a particularly structured Boltzmann machine, which we refer to as a dynamic Boltzmann machine (DyBM), as a stochastic model of a multidimensional time-series. The DyBM can have infinitely many layers of units but allows exact and efficient inference…
Chiara Marullo, Elena Agliari
The Hopfield model and the Boltzmann machine are among the most popular examples of neural networks. The latter, widely used for classification and feature detection, is able to efficiently learn a generative model from observed data and constitutes the benchmark for statistical learning. The former, designed to mimic…
Miguel Aguilera, Manuel G. Bedía
Many biological and cognitive systems do not operate deep into one or other regime of activity. Instead, they exploit critical surfaces poised at transitions in their parameter space. The pervasiveness of criticality in natural systems suggests that there may be general principles inducing this behaviour. However…
Stephon Alexander, Sarah Bawabe, Batia Friedman-Shaw, Michael W. Toomey
This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms which we present via the domain they know best, physics. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising…
Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie
Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally implemented with rigid, temporally non-local, and periodic learning…
Hideyuki Suzuki, Jun-ichi Imura, Yoshihiko Horio, Kazuyuki Aihara
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. Since no randomness is required, efficient hardware…
Guoqi Li, Lei Deng, Yi Xu, Changyun Wen + 3 more
'Luping Shi'] Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key…
Miguel Aguilera, Manuel G. Bedia
The activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of living systems to…
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, Simon Olsson
The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce Surrogate…
Kristina Trifonova, Martin J. Falk, Mason Rouches, Suriyanarayanan Vaikuntanathan + 2 more
Reports of learning in single cells without genetic change span decades yet remain controver-sial, in part because there is no accepted general molecular mechanism for training comparable to gradient-based training or Hebbian learning in neural circuits. Here we identify a minimal set of ingredients sufficient to…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Anupam Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie Amaro
Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this…
Florian Meier, Raphaël Dang-Nhu, Angelika Steger
Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question…
Erdem Pulcu
We are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty^1^ (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…