Intrinsic dimension of data representations in deep neural networks
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[1] Daniel D. Lee,et al. Classification and Geometry of General Perceptual Manifolds , 2017, Physical Review X.
[2] Stefano Soatto,et al. Emergence of Invariance and Disentanglement in Deep Representations , 2017, 2018 Information Theory and Applications Workshop (ITA).
[3] Alessandro Laio,et al. Estimating the intrinsic dimension of datasets by a minimal neighborhood information , 2017, Scientific Reports.
[4] Ken-ichi Kawarabayashi,et al. Estimating Local Intrinsic Dimensionality , 2015, KDD.
[5] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[6] Marcello Pelillo,et al. Characterization of Visual Object Representations in Rat Primary Visual Cortex , 2018, ECCV Workshops.
[7] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[8] Giulio Matteucci,et al. Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex , 2018, The Journal of Neuroscience.
[9] Bruno A Olshausen,et al. Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.
[10] Jascha Sohl-Dickstein,et al. SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability , 2017, NIPS.
[11] Samy Bengio,et al. Insights on representational similarity in neural networks with canonical correlation , 2018, NeurIPS.
[12] Misha Denil,et al. Predicting Parameters in Deep Learning , 2014 .
[13] Tao Yu,et al. Curvature-based Comparison of Two Neural Networks , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).
[14] Kenneth D. Harris,et al. High-dimensional geometry of population responses in visual cortex , 2019, Nat..
[15] Vincenzo Carnevale,et al. Accurate Estimation of the Intrinsic Dimension Using Graph Distances: Unraveling the Geometric Complexity of Datasets , 2016, Scientific Reports.
[16] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[17] James Bailey,et al. The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).
[18] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[19] Ronen Basri,et al. Efficient Representation of Low-Dimensional Manifolds using Deep Networks , 2016, ICLR.
[20] Surya Ganguli,et al. An analytic theory of generalization dynamics and transfer learning in deep linear networks , 2018, ICLR.
[21] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[22] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[23] Peter J. Bickel,et al. Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.
[24] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Yann LeCun,et al. Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks , 2018, ArXiv.
[26] Stefano Panzeri,et al. Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex , 2017, eLife.
[27] David D. Cox,et al. Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .
[28] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[29] Haim Sompolinsky,et al. Linear readout of object manifolds. , 2015, Physical review. E.
[30] Naftali Tishby,et al. Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.
[31] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[32] Vishnu Naresh Boddeti,et al. On the Intrinsic Dimensionality of Image Representations , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Haiping Huang,et al. Mechanisms of dimensionality reduction and decorrelation in deep neural networks , 2018, Physical Review E.
[34] James Bailey,et al. Dimensionality-Driven Learning with Noisy Labels , 2018, ICML.
[35] Dapeng Oliver Wu,et al. Why Deep Learning Works: A Manifold Disentanglement Perspective , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Jakob H. Macke,et al. Analyzing biological and artificial neural networks: challenges with opportunities for synergy? , 2018, Current Opinion in Neurobiology.