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Ensemble algorithms offer state of the art performance in many machine learning applications.
Joint Training of Neural Network Ensembles
Webb, A. M., Reynolds, C., Iliescu, D.-A., Reeve, H., Lujan, M., and Brown, G · 1902
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To Ensemble or Not Ensemble: When does End-To-End Training Fail?
Webb, A. M., Reynolds, C., Chen, W., Reeve, H., Iliescu, D.-A., Lujan, M., and Brown, G · 1902
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 1906
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The Utility of Wealth
Markowitz, H · 1952
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Neural Networks and the Bias/Variance Dilemma, 1992
Geman, S., Bienenstock, E., and Doursat, R · 1992
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Neural Network Ensembles, Cross Validation, and Active Learning
Krogh, A. and Vedelsby, J · 1995
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Bagging predictors
Breiman, L · 1996
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Generalization error of ensemble estimators
Ueda, N. and Nakano, R · 1996
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An empirical evaluation of bagging and boosting
Maclin, R. and Opitz, D · 1997
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An empirical evaluation of bagging and boosting for artificial neural networks
Opitz, D. W. and Maclin, R. F · 1997
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Selecting weighting factors in logarithmic opinion pools
Heskes, T · 1998
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Empirical comparison of voting classification algorithms: bagging, boosting, and variants
Bauer, E. and Kohavi, R · 1999
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Ensemble learning via negative correlation
Liu, Y. and Yao, X · 1999
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A Unified Bias-Variance Decomposition
Domingos, P · 2000
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General bias/variance decomposition with target independent variance of error functions derived from the exponential family of distributions
Hansen, J. V. and Heskes, T · 2000
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Boosting algorithms as gradient descent
Mason, L., Baxter, J., Bartlett, P., and Frean, M · 2000
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Boosting neural networks
Schwenk, H. and Bengio, Y · 2000
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MultiBoosting: a technique for combining boosting and wagging
Webb, G. I · 2000
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Online Bagging and Boosting
Oza, N. C. and Russell, S · 2001
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Variance and bias for general loss functions
James, G. M · 2003
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Creating diversity in ensembles using artificial data
Melville, P. and Mooney, R. J · 2004
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Managing Diversity in Regression Ensembles
Brown, G., WatT, J. L., and Tino, P · 2005
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Extremely randomized trees
Geurts, P., Ernst, D., and Wehenkel, L · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Cited alongside, same era.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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Advanced calculus of several variables
Edwards, C. H · 2012
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Boosting: Foundations and algorithms
Schapire, R. E. and Freund, Y · 2012
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Advanced calculus of several variables
Edwards, C. H · 2012
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Understanding dropout
Baldi, P. and Sadowski, P · 2013
Cited alongside, same era.
Analysis 2
Königsberger, K · 2013
Snapshot ensembles: Train 1, get M for free
Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J. E., and Weinberger, K. Q · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Generalized ambiguity decompositions for classification with applications in active learning and unsupervised ensemble pruning
Jiang, Z., Liu, H., Fu, B., and Wu, Z · 2017
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Generalization in Deep Learning
Kawaguchi, K., Kaelbling, L. P., and Bengio, Y · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Cited alongside, same era.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
Cited alongside, same era.
Analysis 2
Königsberger, K · 2013
Cited alongside, same era.
Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D · 2014
Cited alongside, same era.
Ensemble deep learning for regression and time series forecasting
Qiu, X., Zhang, L., Ren, Y., Suganthan, P., and Amaratunga, G · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
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Understanding deep learning requires rethinking generalization
Zhang, C., Recht, B., Bengio, S., Hardt, M., and Vinyals, O · 2017
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Zhou, Z.-H. and Feng, J · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Coupled ensembles of neural networks
Dutt, A., Pellerin, D., and Quénot, G · 2018
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Learning deep ResNet blocks sequentially using boosting theory
Huang, F., Ash, J. T., Langford, J., and Schapire, R. E · 2018
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A convergence analysis of gradient descent for deep linear neural networks
Arora, S., Golowich, N., Cohen, N., and Hu, W · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M. W., Lee, K., and Toutanova, K · 2019
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Diversity with cooperation: Ensemble methods for few-shot classification
Dvornik, N., Mairal, J., and Schmid, C · 2019
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Searching for mobileNetV3
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L. C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Le, Q., and Adam, H · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. V · 2019
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Binary ensemble neural network: More bits per network or more networks per bit?
Zhu, S., Dong, X., and Su, H · 2019
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Searching for mobileNetV3
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L. C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Le, Q., and Adam, H · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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On-site gamma-hadron separation with deep learning on fpgas
Buschjäger, S., Pfahler, L., Buss, J., Morik, K., and Rhode, W · 2020
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Adabelief optimizer: Adapting stepsizes by the belief in observed gradients
Zhuang, J., Tang, T., Ding, Y., Tatikonda, S., Dvornek, N., Papademetris, X., and Duncan, J · 2020
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Adabelief optimizer: Adapting stepsizes by the belief in observed gradients
Zhuang, J., Tang, T., Ding, Y., Tatikonda, S., Dvornek, N., Papademetris, X., and Duncan, J · 2020
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