Journal ArticleDOI
Pattern Recognition and Machine Learning
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This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
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Efficient Exploration via State Marginal Matching
Lisa Lee,Benjamin Eysenbach,Emilio Parisotto,Eric P. Xing,Sergey Levine,Ruslan Salakhutdinov +5 more
TL;DR: This work recast exploration as a problem of State Marginal Matching (SMM), where it is demonstrated that agents that directly optimize the SMM objective explore faster and adapt more quickly to new tasks as compared to prior exploration methods.
Journal ArticleDOI
The Mixed Instrumental Controller: Using Value of Information to Combine Habitual Choice and Mental Simulation
TL;DR: A novel view in which a single Mixed Instrumental Controller produces both goal-directed and habitual behavior by flexibly balancing and combining model-based and model-free computations is proposed.
Journal ArticleDOI
Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping
TL;DR: The principal advantage of the proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories.
Proceedings ArticleDOI
Extracting urban patterns from location-based social networks
TL;DR: A probabilistic topic models approach to automatically extract urban patterns from location-based social network data is tested and it is found that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.
Journal ArticleDOI
Design of experiments and focused grid search for neural network parameter optimization
Fabricio J. Pontes,G.F. Amorim,Pedro Paulo Balestrassi,Anderson Paulo de Paiva,João Roberto Ferreira +4 more
TL;DR: The proposed tuning method leads to significant reduction of roughness prediction errors in machining operations in comparison to techniques currently used, and constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature.