18,715 Hits in 2.8 sec

Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image [article]

Roman Klokov, Jakob Verbeek, Edmond Boyer
2019 arXiv   pre-print
Moreover, it suggests different options for the image conditioning and allows training in two regimes, using either Monte Carlo or variational approximation of the marginal likelihood.  ...  We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image.  ...  Monte Carlo.  ... 
arXiv:1908.07475v1 fatcat:melrdsf5vzbuffyyhj4df4i47e

An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring

Taha Hassan, Fahad Javed, Naveed Arshad
2014 IEEE Transactions on Smart Grid  
Herein, wave-shape features are found to be an effective new basis of classification and prediction for semi-automated energy disaggregation and monitoring.  ...  We also demonstrate the use of variants of differential evolution as a novel strategy for selection of optimal load models in context of energy disaggregation.  ...  Subsequently Monte Carlo simulations are conducted with these parameters to evaluate . B.  ... 
doi:10.1109/tsg.2013.2271282 fatcat:f6qgolffzfc3re55xfa42ht62u

High Energy Physics Calorimeter Detector Simulation using Generative Adversarial Networks with Domain Related Constraints

Gul Rukh Khattak, Sofia Vallecorsa, Federico Carminati, Gul Muhammad Khan
2021 IEEE Access  
Carlo.  ...  Validation of the results primarily consists of a detailed comparison to full Monte Carlo in terms of several physics quantities where a high level of agreement is found (ranging from a few percent up  ...  This work has been conducted with the support of Intel in the framework of the CERN openlab-Intel collaboration agreement. Part of this work was conducted at "iBanks", the AI GPU cluster at Caltech.  ... 
doi:10.1109/access.2021.3101946 fatcat:xjve25xeqndpdeon44gpkjbqny

Learning Size and Shape of Calabi-Yau Spaces [article]

Magdalena Larfors, Andre Lukas, Fabian Ruehle, Robin Schneider
2021 arXiv   pre-print
We are the first to provide the possibility to compute these metrics for arbitrary, user-specified shape and size parameters of the compact space and observe a linear relation between optimization of the  ...  We present a new machine learning library for computing metrics of string compactification spaces.  ...  The training data consists of Monte-Carlo sampled points on the CY.  ... 
arXiv:2111.01436v1 fatcat:g5v42fzi7nfkjeajpuiuuorq3u

Magnetic Hamiltonian Monte Carlo with Partial Momentum Refreshment

Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala
2021 IEEE Access  
In this work, we combine the sampling benefits of non-canonical Hamiltonian dynamics offered by MHMC with partial momentum refreshment to create the Magnetic Hamiltonian Monte Carlo with Partial Momentum  ...  Magnetic Hamiltonian Monte Carlo (MHMC) has been shown to provide more efficient sampling of the target posterior compared to Hamiltonian Monte Carlo (HMC).  ...  Monte Carlo and Hamiltonian Monte Carlo with partial momentum refreshment on all the performance metrics considered.  ... 
doi:10.1109/access.2021.3101810 fatcat:5gijoj7gujehrpkwugysnpqemi

An empirical analysis of scenario generation methods for stochastic optimization

Nils Löhndorf
2016 European Journal of Operational Research  
This work presents an empirical analysis of popular scenario generation methods for stochastic optimization, including quasi-Monte Carlo, moment matching, and methods based on probability metrics, as well  ...  A controversial finding concerns evidence for the ineffectiveness of widely used methods based on minimizing probability metrics under high-dimensional randomness.  ...  Monte Carlo and Quasi-Monte Carlo A common approach to solve a stochastic optimization problem using SAA is based on Monte Carlo sampling.  ... 
doi:10.1016/j.ejor.2016.05.021 fatcat:cvlbpb6yfrfndk2zqjszuco7q4

Quantum-Inspired Magnetic Hamiltonian Monte Carlo

Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala
2021 PLoS ONE  
Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient  ...  Furthermore, Magnetic Hamiltonian Monte Carlo (MHMC) has been recently proposed as an extension to HMC and adds a magnetic field to HMC which results in non-canonical dynamics associated with the movement  ...  Furthermore, machine learning techniques have also been utilised to enhance the efficiency of Monte Carlo sampling algorithms. In Mcnaughton et al.  ... 
doi:10.1371/journal.pone.0258277 pmid:34610053 pmcid:PMC8491946 fatcat:iatt2xwovnfnfnltoczx7ulwgm

Inverse Transport Networks [article]

Chengqian Che, Fujun Luan, Shuang Zhao, Kavita Bala, Ioannis Gkioulekas
2018 arXiv   pre-print
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material  ...  During training, these networks are evaluated not only in terms of how close they can predict groundtruth parameters, but also in terms of whether the parameters they produce can be used, together with  ...  Differentiable Monte Carlo Rendering Background.  ... 
arXiv:1809.10820v1 fatcat:lf54vpjrjvhuhgtd74jvadlkda

Hamiltonian Monte Carlo for Hierarchical Models [chapter]

Michael Betancourt, Mark Girolami
2015 Current Trends in Bayesian Methodology with Applications  
In this paper we explore the use of Hamiltonian Monte Carlo for hierarchical models and demonstrate how the algorithm can overcome those pathologies in practical applications.  ...  For how long to evolve the system depends on the shape of the target distribution, and the optimal value may vary with position [14] .  ...  Whether using Euclidean Hamiltonian Monte Carlo with careful parameterizations or Riemannian Hamiltonian Monte Carlo with the SoftAbs metric, these algorithms admit inference whose performance scales not  ... 
doi:10.1201/b18502-5 fatcat:g6v3nmp33jgdhhupfgzhd6ytxe

Monte Carlo Tree Search for high precision manufacturing [article]

Dorina Weichert, Felix Horchler, Alexander Kister, Marcus Trost, Johannes Hartung, Stefan Risse
2021 arXiv   pre-print
Monte Carlo Tree Search (MCTS) has shown its strength for a lot of deterministic and stochastic examples, but literature lacks reports of applications to real world industrial processes.  ...  We make use of an expert-knowledge-based simulator and adapt the MCTS default policy to deal with the manufacturing process.  ...  and has been partly funded by the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01IS18038B).  ... 
arXiv:2108.01789v1 fatcat:ukok22yavbdh3ma3zurzxe64ba

Geomstats: A Python Package for Riemannian Geometry in Machine Learning [article]

Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes (+7 others)
2020 arXiv   pre-print
Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport.  ...  Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds.  ...  Monte-Carlo.  ... 
arXiv:2004.04667v1 fatcat:j2fxkjiwijfmne5op2aofeucly

Optimal alarms for vehicular collision detection

Michael Metro, Joydeep Ghosh, Chandra Bhat
2017 2017 IEEE Intelligent Vehicles Symposium (IV)  
Techniques for real-time collision detection are surveyed and grouped into three classes: random Monte Carlo sampling, faster deterministic approximations, and machine learning models trained by simulation  ...  Results validate Monte Carlo sampling as a robust solution despite its simplicity.  ...  The optimal alarm was achieved by a Monte Carlo alarm with 20000 samples, which is too slow for practical use but highly accurate.  ... 
doi:10.1109/ivs.2017.7995732 dblp:conf/ivs/MotroGB17 fatcat:euf6ysjpqbebfmso7k2cjt3tua

A Comparative Study of Nearest Neighbor Regression and Nadaraya Watson Regression

Sarwar A. Hamad, Kawa S. Mohamed Ali
2021 Academic Journal of Nawroz University  
We have proven that under a precise circumstance, the nearest neighborhood estimator and the Nadaraya Watson smoothing produce a smoothed data with a same error level, which means they have the same performance  ...  Carlo of size N=40 with Nadaraya Watson Estimator Table 3 : 3 Monte Carlo of size N=1000 with Nadaraya Watson Estimator Bias MSE Variance MISE Mean 0.0746 0.3897 0.0865 2.7114 Standard  ...  0.0845 0.0363 0.3099 Table 5 : 5 Monte Carlo of size N=1000 with Nearest Figure 7: Bias, Variance, MSE, MISE for N=1000, with Nearest Neighbor Figure 5 is the histograms of the Bias, MSE, Variance  ... 
doi:10.25007/ajnu.v10n2a505 fatcat:r72vdqp6rzew3efdwtr2qbpu24

Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning

Frederik Ruelens, Bert J. Claessens, Stijn Vandael, Bart De Schutter, Robert Babuska, Ronnie Belmans
2017 IEEE Transactions on Smart Grid  
We propose a model-free Monte Carlo method that uses a metric based on the state-action value function or Q-function and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat  ...  Index Terms-Batch reinforcement learning, demand response, electric water heater, fitted Q-iteration, heat pump.  ...  ACKNOWLEDGMENT The authors would like to thank Karel Macek of Honeywell and the reinforcement learning team from the University of Liège for their comments and suggestions.  ... 
doi:10.1109/tsg.2016.2517211 fatcat:7pcblod7k5gabkhsanuvnss7wa

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

Jesus Montes, Elena Gomez, Angel Merchán-Pérez, Javier DeFelipe, Jose-Maria Peña, William W. Lytton
2013 PLoS ONE  
the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative.  ...  In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors.  ...  Once the geometrical models were built, the simulations were carried out with MCell software [35] , exploiting the highly optimized Monte Carlo algorithms that it uses to track the stochastic behavior  ... 
doi:10.1371/journal.pone.0068888 pmid:23894367 pmcid:PMC3720878 fatcat:yvkxdonagnfk7bs2emw57x3kfu
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