GP SSM - Revision history

Jwb: /* Basic Gaussian Process Info */

2018-06-16T00:43:29Z

‎Basic Gaussian Process Info

Jwb: /* Basic Gaussian Process Info */

2018-06-16T00:38:53Z

‎Basic Gaussian Process Info

Dpastorm: /* Papers which are more oriented toward control */

2018-02-05T21:30:38Z

‎Papers which are more oriented toward control

Dpastorm: /* Papers which are more oriented toward control */

2018-02-05T21:29:38Z

‎Papers which are more oriented toward control

Jwb: /* Review-like papers */

2018-01-18T22:28:07Z

‎Review-like papers

Jwb: /* Papers on Deep Learning Theory */

2018-01-17T04:33:53Z

‎Papers on Deep Learning Theory

Jwb: /* Papers on Deep Learning Theory */

2018-01-17T04:30:16Z

‎Papers on Deep Learning Theory

Jwb: /* Papers on Deep Learning Theory */

2018-01-17T04:21:50Z

‎Papers on Deep Learning Theory

Jwb: /* Papers on Deep Learning */

2018-01-17T01:44:27Z

‎Papers on Deep Learning

Jwb: /* Web Links */

2018-01-17T01:43:51Z

‎Web Links

@@ Line 7: / Line 7: @@
 === Basic Gaussian Process Info ===
-* [[Media:RasumussenWilliamsBook.pdf | Rasmussen and Williams]]
+* [[Media:RasumussenWilliamsBook.pdf | Gaussian Processes For Machine Learning]], by Rasmussen and Williams
 === Web Links ===

@@ Line 7: / Line 7: @@
 === Basic Gaussian Process Info ===
-* Rasmussen and Williams
+* [[Media:RasumussenWilliamsBook.pdf | Rasmussen and Williams]]
 === Web Links ===

@@ Line 97: / Line 97: @@
 * A. Surana, [[Media:KoopmanObserverBasedSynthesis.pdf | Koopman Based Operator Synthesis for Control-Affine Noninear Systems]], ''Proc. IEEE Conf. Decision and Control'', 2016
 * A. Surana and A. Banaszuk, [[Media:LinearObserverSynthesis.pdf | Linear Observer Synthesis for Nonlinear Systems Using Koopman Operator Framework]], ''IFAC Papers Online'', 49-18, pp. 716-723, 2016.
-* E. Kaiser, N. Kutz, and S. Brunton, [[Sparse identification of nonlinear dynamics for model predictive control in the low-data limit]] ''arXiv preprint'' arXiv:1711.05501 (2017).
+* E. Kaiser, N. Kutz, and S. Brunton, [[Media: sparseMPC.pdf | Sparse identification of nonlinear dynamics for model predictive control in the low-data limit]] ''arXiv preprint'' arXiv:1711.05501 (2017).
 ==== Papers which are more oriented toward fluids ====

@@ Line 97: / Line 97: @@
 * A. Surana, [[Media:KoopmanObserverBasedSynthesis.pdf | Koopman Based Operator Synthesis for Control-Affine Noninear Systems]], ''Proc. IEEE Conf. Decision and Control'', 2016
 * A. Surana and A. Banaszuk, [[Media:LinearObserverSynthesis.pdf | Linear Observer Synthesis for Nonlinear Systems Using Koopman Operator Framework]], ''IFAC Papers Online'', 49-18, pp. 716-723, 2016.
 ==== Papers which are more oriented toward fluids ====

@@ Line 62: / Line 62: @@
 ==== Review-like papers ====
 * S. Mallat, [[Media:UnderstandingDeepCNNs.pdf | Understanding Deep Convolutional Networks]], ''Phil. Trans. Royal Society, A, vol. 374, May 15, 2017.
 * M.D. Zeiler and R. Fergus, [https://arxiv.org/abs/1311.2901  Visualizing and Understanding Deep Convolutional Networks]

@@ Line 47: / Line 47: @@
 === Papers on Deep Learning ''Theory'' ===
 * This is the paper that we seek to understand in this reading group.
-** A. Achile and S. Soatto, [[Media:InvarianceDisentanglement.pdf | Emergence of Invariance and Disentanglement in Deep Representations]], ''arXiV:1706.01350v2'', Oct. 2017.
+** A. Achille and S. Soatto, [[Media:InvarianceDisentanglement.pdf | Emergence of Invariance and Disentanglement in Deep Representations]], ''arXiV:1706.01350v2'', Oct. 2017.
-* Here are some background papers
+* Here are some background papers (i.e., links to many of the references in the Achille and Soatto paper)
 ** A. Achille and S. Soatto [https://arxiv.org/pdf/1611.01353.pdf  Information Dropout: Learning Optimal Representations Through Noisy Computation]
 ** A. Alemi, I. Fischer, K. Dillon, and K. Murphey, [https://arxiv.org/abs/1612.00410 Deep Variational Information Bottleneck], ''arXiV:1612.00410''
@@ Line 54: / Line 54: @@
 ** Y. Bengio [https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf Learning Deep Architectures for AI], 2009.
 ** J. Bruna and S. Mallat, [http://www.cmap.polytechnique.fr/scattering/scattering_cvpr2011.pdf Classification with Scattering Operators], ''CVPR'', 2011.
-** P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina [https://arxiv.org/abs/1611.01838 Entropy-SGD: Biasing Gradient Descent into Wide Valleys], ''Proceedings of the International Conference on Learning Representations (ICLR)'', 2017
+** P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, R. Zecchina [https://arxiv.org/abs/1611.01838 Entropy-SGD: Biasing Gradient Descent into Wide Valleys], ''Proceedings of the International Conference on Learning Representations (ICLR)'', 2017.
 ** N. Tishby and N. Zaslavsky, [[Media:DeepLearningBottleneck.pdf | Deep Learning and the Information Bottleneck Principle]], ''arXiv:1503.02406'', March, 2015.
-** D.-A. Clevert, T. Unterthiner, and S. Hochreiter, [https://arxiv.org/abs/1511.07289 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)], ''arXiv:1511.07289''
+** D.-A. Clevert, T. Unterthiner, and S. Hochreiter, [https://arxiv.org/abs/1511.07289 Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)], ''arXiv:1511.07289''.
 * P. Chauderi, A.Oberman, S. Osher, S. Soatto, G. Ccarlier, [[Media:DeepNetPDEs.pdf | Deep Relaxation: Partial Differential Equations for Optimizing Deep Neural Networks]], ''arXiV:1704.04932v2''

@@ Line 66: / Line 66: @@
 === Web Links ===
-** [http://deeplearning.net Deeplearning.net]
+* [http://deeplearning.net Deeplearning.net]; [http://deeplearningbook.org/ On-line Deep Learning TextBook]
 == Koopman Spectral Method ==