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A Fast and Accurate Face Detector Based on Neural Networks
TLDR
We propose a fast search algorithm for face detection, based on a new model of neural network. Expand
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Generic Exploration and K-armed Voting Bandits
TLDR
We study a stochastic online learning scheme with partial feedback where the utility of decisions is only observable through an estimation of the environment parameters. Expand
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A fast and accurate face detector for indexation of face images
TLDR
We propose a state-of-the-art generative neural network model, the Constrained Generative Model (CGM). Expand
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A methodology to explain neural network classification
TLDR
Neural networks are still frustrating tools in the data mining arsenal. Expand
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The non-stationary stochastic multi-armed bandit problem
TLDR
We consider a variant of the stochastic multi-armed bandit with K arms where the rewards are not assumed to be identically distributed, but are generated by a non-stationary process. Expand
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EXP3 with drift detection for the switching bandit problem
TLDR
The multi-armed bandit is a model of exploration and exploitation, where one must select, within a finite set of arms, the one which maximizes the cumulative reward up to the time horizon. Expand
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Multi-armed bandit problem with known trend
TLDR
We introduce a new formulation of the multi-armed bandit problem motivated by the real world problem of active learning, music and interface recommendation. Expand
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A Neural Networks Committee for the Contextual Bandit Problem
TLDR
This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Expand
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Contact personalization using a score understanding method
TLDR
This paper presents a method to interpret the output of a classification (or regression) model. Expand
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Contextual Bandit for Active Learning: Active Thompson Sampling
TLDR
The labelling of training examples is a costly task in a supervised classification. Expand
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