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Supervised training of deep neural nets typically relies on minimizing cross-entropy.
Learning to rank using gradient descent
Burges, C. J. C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G · 2005
Earlier work this paper cites.
Loss Functions for Discriminative Training of Energy-Based Models
LeCun, Y. and Huang, F. J · 2005
Earlier work this paper cites.
Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y · 2005
Earlier work this paper cites.
Learning to rank with nonsmooth cost functions
Burges, C. J. C., Ragno, R., and Le, Q. V · 2007
Earlier work this paper cites.
A support vector method for optimizing average precision
Yue, Y., Finley, T., Radlinski, F., and Joachims, T · 2007
Earlier work this paper cites.
Matrix updates for perceptron training of continuous density hidden markov models
Cheng, C.-C., Sha, F., and Saul, L. K · 2009
Earlier work this paper cites.
BoltzRank: Learning to Maximize Expected Ranking Gain
Volkovs, M. N. and Zemel, R. S · 2009
Earlier work this paper cites.
From RankNet to LambdaRank to LambdaMART: An Overview
Burges, C. J. C · 2010
Cited alongside, same era.
Direct loss minimization for structured prediction
McAllester, D. A., Keshet, J., and Hazan, T · 2010
Cited alongside, same era.
Generalization bounds and consistency for latent structural probit and ramp loss
Keshet, J. and McAllester, D. A · 2011
Cited alongside, same era.
Direct Error Rate Minimization of Hidden Markov Models
Keshet, J., Cheng, C.-C., Stoehr, M., and McAllester, D · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Cited alongside, same era.
Structured Output Learning with High Order Loss Functions
Tarlow, D. and Zemel, R. S · 2012
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, A. S. M., van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Efficient Optimization for Average Precision SVM
Mohapatra, P., Jawahar, C. V., and Kumar, M. P · 2014
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Deep learning
Bengio, Y., Goodfellow, I. J., and Courville, A · 2015
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Learning Deep Structured Models
Chen, L.-C., Schwing, A. G., Yuille, A. L., and Urtasun, R · 2015
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Direct loss minimization inverse optimal control
Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., and Schaal, S · 2015
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Selective search for object recognition
Uijlings, J., van de Sande, K., Gevers, T., and Smeulders, A · 2013
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Closest in time.