Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Speech recognition using deep neural networks: A systematic review, IEEE Access, vol.7, pp.19-143, 2019. ,
A survey of the usages of deep learning in natural language processing, 2018. ,
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. ,
An analysis of deep neural network models for practical applications, 2016. ,
, AI benchmark: Running deep neural networks on android smartphones, pp.0-0, 2018.
Are very deep neural networks feasible on mobile devices, IEEE Trans. Circ. Syst. Video Technol, 2016. ,
Ese: Efficient speech recognition engine with sparse lstm on fpga, Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp.75-84, 2017. ,
Energy and policy considerations for deep learning in nlp, ACL, 2019. ,
Green ai, 2019. ,
Predicting parameters in deep learning, Advances in neural information processing systems, pp.2148-2156, 2013. ,
Regularization path for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, pp.1-122, 2010. ,
Statistcal learning with sparsity: The lasso and generalizations, 2015. ,
Learning both weights and connections for efficient neural network, Advances in neural information processing systems, pp.1135-1143, 2015. ,
Learning sparse neural networks via sensitivity-driven regularization, Advances in Neural Information Processing Systems, pp.3878-3888, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01995794
Learning sparse networks using targeted dropout, 2019. ,
Learning-compression algorithms for neural net pruning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
Pruning filters for efficient convnets, 2016. ,
Channel pruning for accelerating very deep neural networks, Proceedings of the IEEE International Conference on Computer Vision, pp.1389-1397, 2017. ,
Network trimming: A datadriven neuron pruning approach towards efficient deep architectures, 2016. ,
Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.49-67, 2006. ,
Group sparse regularization for deep neural networks, Neurocomputing, vol.241, pp.81-89, 2017. ,
Learning structured sparsity in deep neural networks, Advances in neural information processing systems, pp.2074-2082, 2016. ,
A note on the group lasso and a sparse group lasso, 2010. ,
Attentionbased guided structured sparsity of deep neural networks, 2018. ,
Learning efficient convolutional networks through network slimming, Proceedings of the IEEE International Conference on Computer Vision, pp.2736-2744, 2017. ,
A survey of model compression and acceleration for deep neural networks, 2017. ,
Matrix completion by truncated nuclear norm regularization, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012. ,
Dropout as a low-rank regularizer for matrix factorization, International Conference on Artificial Intelligence and Statistics, pp.435-444, 2018. ,
Dropout: a simple way to prevent neural networks from overfitting, The journal of machine learning research, vol.15, issue.1, pp.1929-1958, 2014. ,
Proximité et dualité dans un espace hilbertien, Bull. Soc.Math. France, vol.93, pp.273-299, 1965. ,
Splitting algorithms for the sum of two nonlinear operators, SIAM Journal on Numerical Analysis, vol.16, issue.6, pp.964-979, 1979. ,
Proximal splitting methods in signal processing," in Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011. ,
Solving structured sparsity regularization with proximal methods, Machine Learning and Knowledge Discovery in Databases, pp.418-433, 2010. ,
Optimization for Machine Learning, 2012. ,
The entire regularization path for the support vector machine, Journal of Machine Learning Research, vol.5, pp.1391-1415, 2004. ,
Complexity analysis of the lasso regularization path, Proceedings of the 29th International Conference on Machine Learning, pp.353-360, 2012. ,
Less is more: Towards compact cnns, European Conference on Computer Vision, pp.662-677, 2016. ,
Learning the number of neurons in deep networks, Advances in Neural Information Processing Systems, pp.2270-2278, 2016. ,
Data-driven sparse structure selection for deep neural networks, Proceedings of the European Conference on Computer Vision (ECCV), pp.304-320, 2018. ,
Combined group and exclusive sparsity for deep neural networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.3958-3966, 2017. ,
Learning to share: Simultaneous parameter tying and sparsification in deep learning, 2018. ,
Representational similarity learning with application to brain networks, International Conference on Machine Learning, pp.1041-1049, 2016. ,
Simultaneous sparsity and parameter tying for deep learning using ordered weighted 1 regularization, 2018 IEEE Statistical Signal Processing Workshop (SSP), pp.65-69, 2018. ,
Ordered weighted l1 regularized regression with strongly correlated covariates: Theoretical aspects, Artificial Intelligence and Statistics, pp.930-938, 2016. ,
Toward compact convnets via structure-sparsity regularized filter pruning, IEEE transactions on neural networks and learning systems, 2019. ,
Fast projection onto the simplex and the l1 ball, Mathematical Programming Series A, vol.158, issue.1, pp.575-585, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01056171
Efficient projections onto the l 1-ball for learning in high dimensions, Proceedings of the 25th international conference on Machine learning, pp.272-279, 2008. ,
A filtered bucketclustering method for projection onto the simplex and the 1 -ball, Mathematical Programming, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01562642
Multi-task feature learning via efficient l2, 1-norm minimization, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, ser. UAI '09, pp.339-348, 2009. ,
Robust supervised classification and feature selection using a primal-dual method, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01992399
Classification and regression using an outer approximation projection-gradient method, vol.65, pp.4635-4643, 2017. ,
Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.49-67, 2006. ,
The lottery ticket hypothesis: Finding sparse, trainable neural networks, International Conference on Learning Representations, 2019. ,
Deconstructing lottery tickets: Zeros, signs, and the supermask, Advances in Neural Information Processing Systems, vol.32, pp.3597-3607, 2019. ,
a method for stochastic optimization, ternational Conference on Learning Representations, pp.1-13, 2015. ,
The mnist database of handwritten digits ,
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017. ,
Lets keep it simple, using simple architectures to outperform deeper and more complex architectures, 2016. ,
Energy per instruction trends in intel ® microprocessors, 2006. ,