Fetching the paper…
Reading the bibliography…
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features.
Analysis of a complex of statistical variables into principal components
Hotelling, H · 1933
Earlier work this paper cites.
Statistical theory of extreme values and some practical applications: a series of lectures
Gumbel, E. J · 1954
Earlier work this paper cites.
Genetic algorithms and machine learning
Goldberg, D. E. and Holland, J. H · 1988
Earlier work this paper cites.
Using mutual information for selecting features in supervised neural net learning
Battiti, R · 1994
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
Earlier work this paper cites.
Wrappers for feature subset selection
Kohavi, R. and John, G. H · 1997
Earlier work this paper cites.
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Amaldi, E. and Kann, V · 1998
Earlier work this paper cites.
Extremely randomized trees
Geurts, P., Ernst, D., and Wehenkel, L · 2006
Earlier work this paper cites.
Laplacian score for feature selection
He, X., Cai, D., and Niyogi, P · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
Cited alongside, same era.
The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al · 2006
Cited alongside, same era.
A method for high-throughput gene expression signature analysis
Peck, D., Crawford, E. D., Ross, K. N., Stegmaier, K., Golub, T. R., and Lamb, J · 2006
Cited alongside, same era.
Feature selection using principal feature analysis
Lu, Y., Cohen, I., Zhou, X. S., and Tian, Q · 2007
Cited alongside, same era.
Unsupervised feature selection for multi-cluster data
Cai, D., Zhang, C., and He, X · 2010
Cited alongside, same era.
l2, 1-norm regularized discriminative feature selection for unsupervised learning
An experimental comparison of feature selection methods on two-class biomedical datasets
Drotár, P., Gazda, J., and Smékal, Z · 2015
Later among the works it cites.
Multi-omics of single cells: strategies and applications
Bock, C., Farlik, M., and Sheffield, N. C · 2016
Later among the works it cites.
Gene expression inference with deep learning
Chen, Y., Li, Y., Narayan, R., Subramanian, A., and Xie, X · 2016
Later among the works it cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
Later among the works it cites.
Feature selection: A data perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., and Liu, H · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yang, Y., Shen, H. T., Ma, Z., Huang, Z., and Zhou, X · 2011
Cited alongside, same era.
Pattern classification
Duda, R. O., Hart, P. E., and Stork, D. G · 2012
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Cited alongside, same era.
A feature subset selection algorithm automatic recommendation method
Wang, G., Song, Q., Sun, H., Zhang, X., Xu, B., and Zhou, Y · 2013
Cited alongside, same era.
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
Later among the works it cites.
Han, K., Li, C., and Shi, X · 2017
Later among the works it cites.
More is better: recent progress in multi-omics data integration methods
Huang, S., Chaudhary, K., and Garmire, L. X · 2017
Later among the works it cites.
Learning to explain: An information-theoretic perspective on model interpretation
Chen, J., Song, L., Wainwright, M., and Jordan, M · 2018
Later among the works it cites.