Journal ArticleDOI
Pattern Recognition and Machine Learning
TLDR
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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Journal ArticleDOI
Probabilistic brains: knowns and unknowns
TL;DR: The challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference are discussed.
Posted Content
Black Box Variational Inference
TL;DR: The authors proposed a black box variational inference algorithm based on a stochastic optimization of the variational objective, where the noisy gradient is computed from Monte Carlo samples from the Variational distribution, which can be applied to many models with little additional derivation.
Book
Boosting: Foundations and Algorithms
Robert E. Schapire,Yoav Freund +1 more
TL;DR: This book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics.
Journal ArticleDOI
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
Johannes Hachmann,Roberto Olivares-Amaya,Sule Atahan-Evrenk,Carlos Amador-Bedolla,Carlos Amador-Bedolla,Roel S. Sánchez-Carrera,Aryeh Gold-Parker,Leslie Vogt,Anna M. Brockway,Alán Aspuru-Guzik +9 more
TL;DR: This Perspective introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials, and gives a broad overview of its setup and infrastructure, present first results, and outline upcoming developments.
Journal Article
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Sch "utt,Farhad Arbabzadah,Stefan Chmiela,Klaus-Robert M "uller,Alexandre Tkatchenko +4 more
TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.