mathkann/stat212b

Topics Course on Deep Learning UC Berkeley

stat212b

Topics Course on Deep Learning for Spring 2016

by Joan Bruna, UC Berkeley, Statistics Department

##Syllabus

1st part: Convolutional Neural Networks

• Invariance, stability.
• Variability models (deformation model, stochastic model).
• Scattering
• Extensions
• Group Formalism
• Supervised Learning: classification.
• Properties of CNN representations: invertibility, stability, invariance.
• covariance/invariance: capsules and related models.
• Connections with other models: dictionary learning, LISTA, Random Forests.
• Other tasks: localization, regression.
• Embeddings (DrLim), inverse problems
• Extensions to non-euclidean domains.
• Dynamical systems: RNNs and optimal control.
• Guest Lecture: Wojciech Zaremba (OpenAI)

2nd part: Deep Unsupervised Learning

• Autoencoders (standard, denoising, contractive, etc.)
• Variational Autoencoders
• Adversarial Generative Networks
• Maximum Entropy Distributions
• Open Problems
• Guest Lecture: Soumith Chintala (Facebook AI Research)

3rd part: Miscellaneous Topics

• Non-convex optimization theory for deep networks
• Stochastic Optimization
• Attention and Memory Models
• Guest Lecture: Yann Dauphin (Facebook AI Research)

Schedule

• Lec1 Jan 19: Intro and Logistics

• Lec2 Jan 21: Representations for Recognition : stability, variability. Kernel approaches / Feature extraction. Properties.

  • Elements of Statistical Learning, chapt. 12, Hastie, Tibshirani, Friedman.
  • Understanding Deep Convolutional Networks, S. Mallat.

• Lec3 Jan 26: Groups, Invariants and Filters.

  • Learning Stable Group Invariant Representations with Convolutional Networks
  • Understanding Deep Convolutional Networks, S. Mallat.
  • A Wavelet Tour of Signal Processing, chapt 2-5,7, S. Mallat.

• Lec4 Jan 28: Scattering Convolutional Networks.

  • Invariant Scattering Convolutional Networks

  further reading

  • Group Invariant Scattering, S. Mallat
  • Scattering Representations for Recognition

• Lec5 Feb 2: Further Scattering: Properties and Extensions.

  • Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination, Sifre & Mallat.

• Lec6 Feb 4: Convolutional Neural Networks: Geometry and first Properties.

  • Deep Learning Y. LeCun, Bengio & Hinton.
  • Understanding Deep Convolutional Networks, S. Mallat.

• Lec7 Feb 9: Properties of learnt CNN representations: Covariance and Invariance, redundancy, invertibility

  • Deep Neural Networks with Random Gaussian Weights: A universal Classification Strategy?, R. Giryes, G. Sapiro, A. Bronstein.
  • Intriguing Properties of Neural Networks C. Szegedy et al.
  • Geodesics of Learnt Representations O. Henaff & E. Simoncelli.
  • Inverting Visual Representations with Convolutional Networks, A. Dosovitskiy, T. Brox.
  • Visualizing and Understanding Convolutional Networks M. Zeiler, R. Fergus.

• Lec8 Feb 11: Connections with other models (DL, Lista, Random Forests, CART)

• Proximal Splitting Methods in Signal Processing Combettes & Pesquet.

• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems Beck & Teboulle

• Learning Fast Approximations of Sparse Coding K. Gregor & Y. LeCun

• Task Driven Dictionary Learning J. Mairal, F. Bach, J. Ponce

• Exploiting Generative Models in Discriminative Classifiers T. Jaakkola & D. Haussler

• Improving the Fisher Kernel for Large-Scale Image Classification F. Perronnin et al.

• NetVLAD R. Arandjelovic et al.

• Lec9 Feb 16: Other high level tasks: localization, regression, embedding, inverse problems.

• Object Detection with Discriminatively Trained Deformable Parts Model Felzenswalb, Girshick, McAllester and Ramanan, PAMI'10

• Deformable Parts Models are Convolutional Neural Networks, Girshick, Iandola, Darrel and Malik, CVPR'15.

• Rich Feature Hierarchies for accurate object detection and semantic segmentation Girshick, Donahue, Darrel and Malik, PAMI'14.

• Graphical Models, message-passing algorithms and convex optimization M. Wainwright.

• Conditional Random Fields as Recurrent Neural Networks Zheng et al, ICCV'15

• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Tompson, Jain, LeCun and Bregler, NIPS'14.

• Lec10 Feb 18: Extensions to non-Euclidean domain. Representations of stationary processes. Properties.

• Dimensionality Reduction by Learning an Invariant Mapping Hadsell, Chopra, LeCun,'06.

• Deep Metric Learning via Lifted Structured Feature Embedding Oh Song, Xiang, Jegelka, Savarese,'15.

• Spectral Networks and Locally Connected Networks on Graphs Bruna, Szlam, Zaremba, LeCun,'14.

• Spatial Transformer Networks Jaderberg, Simonyan, Zisserman, Kavukcuoglu,'15.

• Intermittent Process Analysis with Scattering Moments Bruna, Mallat, Bacry, Muzy,'14.

• Lec11 Feb 23: Guest Lecture ( W. Zaremba, OpenAI ) Discrete Neural Turing Machines.

• Lec12 Feb 25: Representations of Stationary Processes (contd). Sequential Data: Recurrent Neural Networks.

  • Intermittent Process Analysis with Scattering Moments J.B., Mallat, Bacry and Muzy, Annals of Statistics,'13.
  • A mathematical motivation for complex-valued convolutional networks Tygert et al., Neural Computation'16.
  • Texture Synthesis Using Convolutional Neural Networks Gatys, Ecker, Betghe, NIPS'15.
  • A Neural Algorithm of Artistic Style, Gatys, Ecker, Betghe, '15.
  • Time Series Analysis and its Applications Shumway, Stoffer, Chapter 6.
  • Deep Learning Goodfellow, Bengio, Courville,'16. Chapter 10.

• Lec13 Mar 1: Recurrent Neural Networks (contd). Long Short Term Memory. Applications.

• Deep Learning Goodfellow, Bengio, Courville,'16. Chapter 10.

• Generating Sequences with Recurrent Neural Networks A. Graves.

• The Unreasonable Effectiveness of Recurrent Neural Networks A. Karpathy

• The Unreasonable effectiveness of Character-level Language Models Y. Goldberg

• Lec14 Mar 3: Unsupervised Learning: Curse of dimensionality, Density estimation. Graphical Models, Latent Variable models.

  • Describing Multimedia Content Using Attention-based Encoder-Decoder Networks K. Cho, A. Courville, Y. Bengio
  • Graphical Models, Exponential Families and Variational Inference M. Wainwright, M. Jordan.

• Lec15 Mar 8: Autoencoders. Variational Inference. Variational Autoencoders.

  • Graphical Models, Exponential Families and Variational Inference, chapter 3 M. Wainwright, M. Jordan.
  • Variational Inference with Stochastic Search J.Paisley, D. Blei, M.Jordan.
  • Stochastic Variational Inference M. Hoffman, D. Blei, Wang, Paisley.
  • Auto-Encoding Variational Bayes, Kingma & Welling.
  • Stochastic Backpropagation and variational inference in deep latent gaussian models D. Rezende, S. Mohamed, D. Wierstra.

• Lec16 Mar 10: Variational Autoencoders (contd). Normalizing Flows. Adversarial Generative Networks.

  • Semi-supervised learning with Deep generative models Kingma, Rezende, Mohamed, Welling.
  • Importance Weighted Autoencoders Burda, Grosse, Salakhutdinov.
  • Variational Inference with Normalizing Flows Rezende, Mohamed.
  • Unsupervised Learning using Nonequilibrium Thermodynamics Sohl-Dickstein et al.
  • Generative Adversarial Networks, Goodfellow et al.

• Lec17 Mar 29: Adversarial Generative Networks (contd).

  • Generative Adversarial Networks, Goodfellow et al.
  • Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Denton, Chintala, Szlam, Fergus.
  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford, Metz, Chintala.

• Lec18 Mar 31: Maximum Entropy Distributions. Self-supervised models (analogies, video prediction, text, word2vec).

  • Graphical Models, Exponential Families and Variational Inference, chapter 3 M. Wainwright, M. Jordan.
  • An Introduction to MCMC for Machine Learning Andrieu, de Freitas, Doucet, Jordan.
  • Stochastic relaxation, Gibbs distributions and the Bayesian Restoration of Images Geman & Geman.
  • Distributed Representations of Words and Phrases and their compositionality Mikolov et al.
  • word2vec Explained: deriving Mikolov et al's negative-sampling embedding method Goldberg & Levy.

• Lec19 Apr 5: Self-supervised models (contd). Non-convex Optimization. Stochastic Optimization.

  • Pixel Recurrent Neural Networks A. van den Oord, N. Kalchbrenner, K. Kavukcuoglu.
  • The tradeoffs of Large Scale Learning Bottou, Bousquet.
  • Introduction to Statistical Learning Theory Bousquet, Boucheron, Lugosi.

• Lec20 Apr 7: Guest Lecture (S. Chintala, Facebook AI Research)

• Lec21 Apr 12: Accelerated Gradient Descent, Regularization, Dropout.

  • Convex Optimization: Algorithms and Complexity S. Bubeck
  • Optimization, Simons Big Data Boot Camp B. Recht
  • The Zen of Gradient Descent M. Hardt.
  • Train Faster, Generalize Better: Stability of Stochastic Gradient Descent M. Hardt, B. Recht, Y. Singer.
  • Dropout: a simple way to prevent neural networks from Overfitting Srivastava, Hinton et al.

• Lec22 Apr 14: Dropout (contd). Batch Normalization, Tensor Decompositions.

  • Dropout Training as Adaptive Regularization Wager, Wang, Liang.
  • Batch Normalization: accelerating Deep Network Training by Reducing internal covariate shift Ioffe, Szegedy.
  • Global Optimality in Tensor Factorization, Deep Learning and Beyond Haefflele, Vidal.
  • On the expressive power of Deep Learning: a tensor analysis Cohen, Sharir, Shashua.
  • Beating the Perils of non-convexity: Guaranteed Training of Neural Networks using Tensor methods Janzamin, Sedghi, Anandkumar.

• Lec23 Apr 19: Guest Lecture (Y. Dauphin, Facebook AI Research)

• Lec24-25: Oral Presentations