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The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches.
The coincidence approach to stochastic point processes
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Determinantal point processes for machine learning
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Frédéric Lavancier, Jesper Møller, and Ege Holger Rubak · 2012
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A Cengiz Öztireli and Markus Gross · 2012
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Efficient estimation of word representations in vector space
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Accelerating minibatch stochastic gradient descent using stratified sampling
Quantifying repulsiveness of determinantal point processes
Christophe Ange Napoléon Biscio, Frédéric Lavancier, et al · 2016
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Adaptive sampling for SGD by exploiting side information
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Batched gaussian process bandit optimization via determinantal point processes
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Multi-class anisotropic blue noise sampling for discrete element pattern generation
Naoki Kita and Kazunori Miyata · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Active bias: Training a more accurate neural network by emphasizing high variance samples
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General point sampling with adaptive density and correlations
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Determinantal point processes for mini-batch diversification
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