暂无分享,去创建一个
Miguel R. D. Rodrigues | Raja Giryes | Daniel Jakubovitz | R. Giryes | M. Rodrigues | Daniel Jakubovitz
[1] Matus Telgarsky,et al. Spectrally-normalized margin bounds for neural networks , 2017, NIPS.
[2] Yi Zhang,et al. Stronger generalization bounds for deep nets via a compression approach , 2018, ICML.
[3] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[4] Nikhil Ketkar. Training Deep Learning Models , 2017 .
[5] David A. McAllester. Simplified PAC-Bayesian Margin Bounds , 2003, COLT.
[6] Michael Elad,et al. Convolutional Neural Networks Analyzed via Convolutional Sparse Coding , 2016, J. Mach. Learn. Res..
[7] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[8] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[9] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[10] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[11] Andrew R. Barron,et al. Approximation and Estimation for High-Dimensional Deep Learning Networks , 2018, ArXiv.
[12] Razvan Pascanu,et al. Sharp Minima Can Generalize For Deep Nets , 2017, ICML.
[13] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[14] Lorenzo Rosasco,et al. Theory of Deep Learning III: explaining the non-overfitting puzzle , 2017, ArXiv.
[15] Elad Hoffer,et al. Train longer, generalize better: closing the generalization gap in large batch training of neural networks , 2017, NIPS.
[16] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[17] Leonardo Rey Vega,et al. Information Bottleneck and Representation Learning , 2021, Information-Theoretic Methods in Data Science.
[18] Rémi Gribonval,et al. Sample Complexity of Dictionary Learning and Other Matrix Factorizations , 2013, IEEE Transactions on Information Theory.
[19] Abbas Mehrabian,et al. Nearly-tight VC-dimension bounds for piecewise linear neural networks , 2017, COLT.
[20] Ryota Tomioka,et al. Norm-Based Capacity Control in Neural Networks , 2015, COLT.
[21] Aleksander Madry,et al. Adversarially Robust Generalization Requires More Data , 2018, NeurIPS.
[22] Dan Boneh,et al. The Space of Transferable Adversarial Examples , 2017, ArXiv.
[23] Yann LeCun,et al. Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks , 2018, ArXiv.
[24] Ryan P. Adams,et al. Compressibility and Generalization in Large-Scale Deep Learning , 2018, ArXiv.
[25] Shie Mannor,et al. The Sample Complexity of Dictionary Learning , 2010, COLT.
[26] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[27] Nathan Srebro,et al. The Implicit Bias of Gradient Descent on Separable Data , 2017, J. Mach. Learn. Res..
[28] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[29] Naftali Tishby,et al. Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.
[30] René Vidal,et al. Global Optimality in Neural Network Training , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Barbara Hammer,et al. Incremental learning algorithms and applications , 2016, ESANN.
[32] Guillermo Sapiro,et al. DNN or k-NN: That is the Generalize vs. Memorize Question , 2018, ArXiv.
[33] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[34] Stéphane Mallat,et al. Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.
[35] Jascha Sohl-Dickstein,et al. Sensitivity and Generalization in Neural Networks: an Empirical Study , 2018, ICLR.
[36] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[37] Guillermo Sapiro,et al. Robust Large Margin Deep Neural Networks , 2016, IEEE Transactions on Signal Processing.
[38] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[39] Rémi Gribonval,et al. Sparse and Spurious: Dictionary Learning With Noise and Outliers , 2014, IEEE Transactions on Information Theory.
[40] Naftali Tishby,et al. Deep learning and the information bottleneck principle , 2015, 2015 IEEE Information Theory Workshop (ITW).
[41] David A. McAllester. PAC-Bayesian model averaging , 1999, COLT '99.
[42] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[43] Lorenzo Rosasco,et al. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review , 2016, International Journal of Automation and Computing.
[44] David A. McAllester,et al. A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks , 2017, ICLR.
[45] Ohad Shamir,et al. Size-Independent Sample Complexity of Neural Networks , 2017, COLT.
[46] Barnabás Póczos,et al. On the Reconstruction Risk of Convolutional Sparse Dictionary Learning , 2017, ArXiv.
[47] David A. McAllester. Some PAC-Bayesian Theorems , 1998, COLT' 98.
[48] Barnabás Póczos,et al. Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning , 2018, AISTATS.
[49] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[50] Yoram Singer,et al. Train faster, generalize better: Stability of stochastic gradient descent , 2015, ICML.
[51] Andrew M. Saxe,et al. High-dimensional dynamics of generalization error in neural networks , 2017, Neural Networks.
[52] Karin Schnass,et al. Convergence radius and sample complexity of ITKM algorithms for dictionary learning , 2015, Applied and Computational Harmonic Analysis.
[53] Peter L. Bartlett,et al. Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks , 1998, Neural Computation.
[54] Gintare Karolina Dziugaite,et al. Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data , 2017, UAI.
[55] Shai Shalev-Shwartz,et al. SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data , 2017, ICLR.
[56] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[57] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[58] Yonina C. Eldar,et al. Performance limits of dictionary learning for sparse coding , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).
[59] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[60] Raja Giryes,et al. Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization , 2018, ECCV.
[61] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[62] Shie Mannor,et al. Robustness and generalization , 2010, Machine Learning.
[63] Leonardo Rey Vega,et al. Compression-Based Regularization With an Application to Multitask Learning , 2017, IEEE Journal of Selected Topics in Signal Processing.
[64] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[65] Hongyang Zhang,et al. Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex , 2018, ArXiv.
[66] Joan Bruna,et al. Mathematics of Deep Learning , 2017, ArXiv.
[67] Guillermo Sapiro,et al. Generalization Error of Invariant Classifiers , 2016, AISTATS.