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
Citations
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Journal ArticleDOI
Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit.
Makoto Ito,Kenji Doya +1 more
TL;DR: Computational issues in reinforcement learning are reviewed and a working hypothesis on how multiple reinforcement learning algorithms are implemented in the cortico-basal ganglia circuit using different representations of states, values, and actions is proposed.
Proceedings ArticleDOI
Stochastic skyline route planning under time-varying uncertainty
TL;DR: A multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network is defined and efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time are proposed.
Proceedings ArticleDOI
Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting
Ming Sun,Anirudh Raju,George Tucker,Sankaran Panchapagesan,Geng-Shen Fu,Arindam Mandal,Spyros Matsoukas,Nikko Strom,Shiv Naga Prasad Vitaladevuni +8 more
TL;DR: This work proposes a max-pooling based loss function for training Long Short-Term Memory networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements and results show that LSTM models trained using cross-entropy loss or max- Pooling loss outperform a cross-ENTropy loss trained baseline feed-forward Deep Neural Network (DNN).
Proceedings ArticleDOI
Vector approximate message passing for the generalized linear model
TL;DR: Numerical experiments show that the proposed GLM-VAMP is much more robust to ill-conditioning in A than damped GAMP.
Posted Content
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
TL;DR: In this article, the basic ideas of principal component analysis (PCA) and kernel PCA are reviewed, and the reconstruction of pre-images for Kernel PCA is discussed.