Fetching the paper…
Reading the bibliography…
Neural networks and tree ensembles are state-of-the-art learners, each with its unique statistical and computational advantages.
Classification and regression trees
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J · 1983
Earlier work this paper cites.
Hierarchical mixtures of experts and the em algorithm
Jordan, M. I. and Jacobs, R. A · 1994
Earlier work this paper cites.
A system for induction of oblique decision trees
Murthy, S. K., Kasif, S., and Salzberg, S · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Friedman, J. H · 2001
Earlier work this paper cites.
Texturing & modeling: a procedural approach
Ebert, D. S., Musgrave, F. K., Peachey, D., Perlin, K., and Worley, S · 2003
Earlier work this paper cites.
The elements of statistical learning: data mining, inference, and prediction
Hastie, T., Tibshirani, R., and Friedman, J · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
OpenGL shading language
Rost, R. J., Licea-Kane, B., Ginsburg, D., Kessenich, J., Lichtenbelt, B., Malan, H., and Weiblen, M · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
Bergstra, J., Yamins, D., and Cox, D. D · 2013
Earlier work this paper cites.
Learning nonlinear functions using regularized greedy forest
Johnson, R. and Zhang, T · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Conditional computation in neural networks for faster models
Bengio, E., Bacon, P., Pineau, J., and Precup, D · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Simultaneous learning of trees and representations for extreme classification and density estimation
Jernite, Y., Choromanska, A., and Sontag, D · 2017
Later among the works it cites.
Lightgbm: A highly efficient gradient boosting decision tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
Later among the works it cites.
Pmlb: a large benchmark suite for machine learning evaluation and comparison
Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., and Moore, J. H · 2017
Later among the works it cites.
Compact multi-class boosted trees
Ponomareva, N., Colthurst, T., Hendry, G., Haykal, S., and Radpour, S · 2017
Later among the works it cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q. V., Hinton, G. E., and Dean, J · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kontschieder, P., Fiterau, M., Criminisi, A., and Bulò, S. R · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Cited alongside, same era.
Decision forests, convolutional networks and the models in-between, 2016
Ioannou, Y., Robertson, D., Zikic, D., Kontschieder, P., Shotton, J., Brown, M., and Criminisi, A · 2016
Cited alongside, same era.
Automatic Speech Recognition
Yu, D. and Deng, L · 2016
Cited alongside, same era.
Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G. E · 2017
Cited alongside, same era.
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Later among the works it cites.
Learning deep nearest neighbor representations using differentiable boundary trees
Zoran, D., Lakshminarayanan, B., and Blundell, C · 2017
Later among the works it cites.
Tanno, R., Arulkumaran, K., Alexander, D. C., Criminisi, A., and Nori, A · 2018
Later among the works it cites.
Neural random forests
Biau, G., Scornet, E., and Welbl, J · 2019
Later among the works it cites.
End-to-end learning of decision trees and forests
Hehn, T. M., Kooij, J. F., and Hamprecht, F. A · 2019
Later among the works it cites.
Learning hierarchical interactions at scale: A convex optimization approach
Hazimeh, H. and Mazumder, R · 2020
Closest in time.