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We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy.
Extensions of Lipschitz mappings into a Hilbert space
Johnson, W. and Lindenstrauss, J. (1984) · 1984
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Similarity estimation techniques from rounding algorithms
Charikar, M. (2002) · 2002
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On the dependence of the Berry–Esseen bound on dimension
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Ailon, N. and Chazelle, B. (2006) · 2006
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Random features for large-scale kernel machines
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Spherical LSH for approximate nearest neighbor search on unit hypersphere
Terasawa, K. and Tanaka, Y. (2007) · 2007
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Random projection trees and low dimensional manifolds
Dasgupta, S. and Freund, Y. (2008) · 2008
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Dense fast random projections and lean Walsh transforms
Liberty, E., Ailon, N., and Singer, A. (2008) · 2008
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Kernel methods for deep learning
Cho, Y. and Saul, L. K. (2009) · 2009
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A sparse johnson: Lindenstrauss transform
Dasgupta, A., Kumar, R., and Sarlos, T. (2010) · 2010
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An almost optimal unrestricted fast Johnson-Lindenstrauss transform
Ailon, N. and Liberty, E. (2011) · 2011
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Johnson-lindenstrauss lemma for circulant matrices
Hinrichs, A. and Vybíral, J. (2011) · 2011
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A variant of the Johnson-Lindenstrauss lemma for circulant matrices
Vybíral, J. (2011) · 2011
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Har-Peled, S., Indyk, P., and Motwani, R. (2012) · 2012
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Denil, M., Shakibi, B., Dinh, L., Ranzato, M., and Freitas, N. D. (2013) · 2013
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Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., and Ramabhadran, B. (2013) · 2013
Practical and optimal LSH for angular distance
Andoni, A., Indyk, P., Laarhoven, T., Razenshteyn, I. P., and Schmidt, L. (2015) · 2015
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Random feature mapping with signed circulant matrix projection
Feng, C., Hu, Q., and Liao, S. (2015) · 2015
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Factoring matrices into the product of circulant and diagonal matrices
Huhtanen, M. and Perämäki, A. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
Pilanci, M. and Wainwright, M. J. (2015) · 2015
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Structured transforms for small-footprint deep learning
Sindhwani, V., Sainath, T. N., and Kumar, S. (2015) · 2015
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Bounds on determinants of perturbed diagonal matrices
Brent, R. P., Osborn, J. H., and Smith, W. D. (2014) · 2014
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Scalable kernel methods via doubly stochastic gradients
Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M.-F., and Song, L. (2014) · 2014
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Kernel methods match deep neural networks on timit
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Convolutional kernel networks
Mairal, J., Koniusz, P., Harchaoui, Z., and Schmid, C. (2014) · 2014
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Randomized sketches of convex programs with sharp guarantees
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Deep fried convnets
Yang, Z., Moczulski, M., Denil, M., de Freitas, N., Smola, A., Song, L., and Wang, Z. (2015) · 2015
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Binary embeddings with structured hashed projections
Choromanska, A., Choromanski, K., Bojarski, M., Jebara, T., Kumar, S., and LeCun, Y. (2016) · 2016
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Recycling randomness with structure for sublinear time kernel expansions
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Acdc: A structured efficient linear layer
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