Intelligent Systems for Pattern Recognition - 9 CFU

Topic outline

• 

  Intelligent Systems for Pattern Recognition - 9 CFU

  Code: 760AA, Credits (ECTS): 9, Semester: 2, Official Language: English
  

  Instructor: Davide Bacciu 

  Contact: email - phone 050 2212749

  Office: Room 367, Dipartimento di Informatica, Largo B. Pontecorvo 3, Pisa

  Office Hours: (email to arrange meeting)
  

  Supporting Instructor:  Antonio Carta (email)
  

  • Course News and Announcements Forum

    Click to access recent news on the course
• 

  Course Information

  Weekly Schedule

  The course is held on the second term. The tentative schedule for A.A. 2021/22 is provided in table below.
  

  The first lecture of the course will be on Tuesday 15/02/2022. The course will be hybrid, both in person and online on the dedicated MS Team.
  

  Recordings of the lectures will be made available to the students following the course.

  Day	Time
  Tuesday	14.15-16.00 (Room D1, Teams meeting)
  Wednesday	16.15-18.00 (Room A1, Teams meeting)
  Thursday	14.15-16.00 (Room A1, Teams meeting)

  
  

  Objectives

  Course Prerequisites

  Course prerequisites include knowledge of machine learning fundamentals (e.g. covered through ML course). Knowledge of elements of probability and statistics, calculus and optimization algorithms are also expected. Previous programming experience with Python is a plus for the practical lectures.
  

  Course Overview

  The course introduces students to the analysis and design of advanced machine learning and deep learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques.
  

  The course is articulated in five parts. The first part introduces basic concepts and algorithms concerning traditional pattern recognition, in particular as pertains sequence and image analysis. The next two parts introduce advanced models from two major learning paradigms, that are deep neural networks and generative models, and their use in pattern recognition applications. The fourth part will introduce fundamentals of reinforcement learning and deep reinforcement learning. The final part of the course will present selected recent works, models and applications of learning models.
  

  Presentation of the theoretical models and associated algorithms will be complemented by introductory classes on the most popular software libraries used to implement them.
  

  The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models.
  

  The official language of the course is English: all materials, references and books are in English. Lecture slides will be made available here, together with suggested readings.

  Topics covered -Bayesian learning, graphical models, learning with sampling and variational approximations, fundamentals of deep learning (CNNs, AE, DBN, GRNs), deep learning for machine vision and signal processing, advanced deep learning models (transformers, VAE, GANs, NTMs), deep graph networks, reinforcement learning and deep reinforcement learning, signal processing and time-series analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bio-informatics, robotics, medical imaging, etc), introduction to programming libraries and frameworks.

  
  

  Textbooks and Teaching Materials

  The course does not have an official textbook covering all its contents. However, a list of reference books covering parts of the course is listed at the bottom of this section (note that all of them have an electronic version freely available online).

  [BRML] David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press (PDF)
  

  [DL] Ian Goodfellow and Yoshua Bengio and Aaron Courville , Deep Learning, MIT Press (ONLINE)
  

  [RL] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Second Edition, MIT Press, 2018 (PDF)
  

  
  
• 

  Introduction (2h)

  Introduction to the course philosophy, its learning goals and expected outcomes. We will discuss prospectively the overall structure of the course and the interelations between its parts. Exam modalities and schedule are also discussed.

  	Date	Topic	References	Additional Material
  1	15/02/2022 (14-16)	Introduction to the course Motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications

  
  

  • Lecture 1 - slides File
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  Fundamentals of Pattern Recognition (6h)

  The module will provide a brief introduction to classical pattern recognition for signal/timeseries and for images. We will cover approaches working on the spatial (temporal) and frequency (spectral) domain, presenting methods to represent temporal and visual information in static descriptors, as well as approaches to identify relevant patterns in the data (feature descriptors). Methodologies covered include correlation analysis, Fourier analysis, wavelets, intensity gradient-based descriptors and detectors, normalized cut segmentation.
  

  	Date	Topic	References	Additional Material
  2	16/02/2022 (16-18)	Signal processing Timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.
  3	17/02/2022 (14-16)	Image Processing I  Spatial feature descriptors (color histograms, SIFT); spectral analysis.		Additional readings  [1] Survey on visual descriptors
  4	22/02/2022 (14-16)	Image Processing II  Feature detectors (edge, blobs); image segmentation; wavelet decompositions		Additional readings  [2] Survey on visual feature detectors Software A wavelet browser to visualize some popular wavelet families and their instances, powered by the PyWavelet library.

  
  

  • Lecture 2 - Slides File
  • Lecture 3 - slides File
  • Lecture 4 - slides (visual detectors) File
  • Lecture 4 - slides (wavelets) File
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  Generative Learning (20h)

  The module introduces probabilistic learning, causal models, generative modelling and Bayesian learning. We will discuss fundamental algoritms and concepts, including Expectation-Maximization, sampling and variational approximations, and we will study relevant models from the three fundamental paradigms of probabilistic learning, namely Bayesian networks, Markov networks and dynamic models.  Models covered include: Bayesian Networks, Hidden Markov Models, Markov Random Fields, Boltzmann Machines,  Latent topic models.

  
  

  	Date	Topic	References	Additional Material
  5	23/02/2022 (16-18)	Introduction to Generative and Graphical Models. Probability refresher; graphical model representation; directed and undirected models	[BRML] Ch. 1 and 2 (Refresher) [BRML] Sect. 3.1, 3.2 and 3.3.1 (conditional independence)	Software Pyro - Python library based on PyTorch PyMC3 - Python library based on Theano Edward - Python library based on TensorFlow TensorFlow Probability - Probabilistic models and deep learning in Tensorflow
  6	24/02/2022 (14-16)	Conditional independence and causality - Part I Bayesian networks; Markov networks; conditional independence;	[BRML] Sect. 3.3 (Directed Models) [BRML] Sect. 4.1, 4.2.0-4.2.2 (Undirected Models) [BRML] Sect. 4.5 (Expressiveness)
  7	01/03/2022 (14-16)	Conditional independence and causality - Part II d-separation; structure learning in Bayesian Networks	[BRML] Sect. 9.5.1 (PC algorithm) [BRML] Sect. 9.5.2 (Independence testing) [BRML] Sect. 9.5.3 (Structure scoring)	Additional readings [3] A short review of BN structure learning [4] PC algorithm with consistent ordering for large scale data [5] MMHC - Hybrid structure learning algorithm Software - A selection of BN structure learning libraries in Python: pgmpy, bnlearn, pomegranate. - bnlearn: the most consolidated and efficient library for BN structure learning (in R) - Causal learner: a mixed R-Matlab package integrating over 26 BN structure learning algorithms.
  8	02/03/2022 (16-18)	Hidden Markov Models  - Part I learning in directed graphical models; forward-backward algorithm;  generative models for sequential data	[BRML] Sect. 23.1.0 (Markov Models) [BRML] Sect. 23.2.0-23.2.4 (HMM and forward backward)	Additional Readings [6]  A classical tutorial introduction to HMMs
  9	03/03/2022 (14-16)	Hidden Markov Models - Part II EM algorithm, learning as inference, Viterbi Algorithm	[BRML] Sect. 23.2.6 (Viterbi) [BRML] Sect. 23.3.1-23.3.4 (EM and learning)
  10	08/03/2022 (14-16)	HMM III + Markov Random Fields learning in undirected graphical  models; conditional random fields; pattern recognition applications	[BRML] Sect. 4.2.2, 4.2.5 (MRF) [BRML] Sect. 4.4 (Factor Graphs) [BRML] Sect. 5.1.1 (Variable Elimination and Inference on Chain)  [BRML] Sect. 9.6.0, 9.6.1, 9.6.4, 9.6.5 (Learning in MRF/CRF)	Additional Readings [7,8] Two comprehensive tutorials on CRF ([7] more introductory and [8] more focused on vision) [9] A nice application of CRF to image segmentation Sofware Check out pgmpy: it has Python notebooks to introduce to working with MRF/CRF
  11	09/03/2022 (16-18)	Bayesian Learning I Principles of Bayesian learning; EM algorithm objective; principles of variational approximation	[BRML] Sect. 11.2.1 (Variational EM)
  12	10/03/2022 (14-16)	Bayesian Learning II latent topic models; Latent Dirichlet Allocation; machine vision application of latent topic models	[BRML] Sect. 20.4-20.6.1  (LDA)	Additional Readings [10] LDA foundation paper [11] A gentle introduction to latent topic models [12] Foundations of bag of words image representation Sofware A didactic Matlab demo of bag-of-words for images A standalone Matlab toolbox for latent topic models (including LDA examples, but discontinued) and the official Matlab LDA implementation A chatty demo on BOW image representation in Python Yet another Python implementation of image BOW
  13	14/03/2022 (11-13) ROOM C1	Bayesian Learning III sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methods Guest lecture by Daniele Castellana	[BRML] Sect. 27.1-27.3, 27.4.1, 27.6.2	Additional Readings [13] A step-by-step derivation of collapsed Gibbs sampling for LDA
  14	15/03/2022 (14-16)	Boltzmann Machines bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence	[DL] Sections 20.1 and 20.2	Additional Readings [14] A clean and clear introduction to RBM from its author Sofware Matlab code for Deep Belief Networks (i.e. stacked RBM) and Deep Boltzmann Machines.

   
  

  • Lecture 5 - slides File
  • Lecture 6-7 - slides File
  • Lecture 8-9 - slides File
  • Lecture 10 slides File
  • Lecture 11-12 slides File
  • Lecture 13 slides File
  • Lecture 14 slides File
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  Deep Learning (24h)

  The module presents the fundamental concepts, challenges, architectures and methodologies of deep learning. We introduce the learning of neural representations from vectorial, sequential and image data, covering both supervised and unsupervised learning, and hinting at various forms of weak supervision. We close the gap between neural networks and probabilistic learning by discussing generative deep learning models. Models covered include: deep autoencoders, convolutional neural networks, long-short term memory, gated recurrent units, deep reservoir computing, sequence-to-sequence, neural attention, neural Turing machines, variational autoencoders, generative adversarial networks. Methodological lectures will be complemented by introductory seminars to Keras-TF and Pytorch.
  

  	Date	Topic	References	Additional Material
  15	16/03/2022 (16-18)	Convolutional Neural Networks I Introduction to the deep learning module; introduction to CNN; basic CNN elements	[DL] Chapter 9	Additional Readings [15-19] Original papers for LeNet, AlexNet, VGGNet, GoogLeNet and ResNet.
  16	17/03/2022 (14-16)	Convolutional Neural Networks II CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code	[DL] Chapter 9	Additional Readings [20] Complete summary of convolution arithmetics [21] Seminal paper on batch normalization [22] CNN interpretation using deconvolutions [23] CNN interpretation with GradCAM
  	22/03/2022 (14-16)	LECTURE CANCELLED (RECOVERED ON THE 14th MARCH)
  	23/03/2022 (16-18)	LECTURE CANCELLED (RECOVERED ON THE 25th MARCH)
  	24/03/2022 (14-16)	LECTURE CANCELLED (RECOVERED ON THE 28th MARCH)
  17	25/03/2022 (14-16) ROOM D1 + ONLINE	Deep Autoencoders Sparse, denoising and contractive AE; deep RBM	[DL] Chapter 14, Sect 20.3, 20.4.0 (from 20.4.1 onwards not needed)	Additional Readings [24] DBN: the paper that started deep learning [25] Deep Boltzmann machines paper [26] Review paper on deep generative models [27] Long review paper on autoencoders from the perspective of representation learning [28] Paper discussing regularized autoencoder as approximations of likelihood gradient
  18	28/03/2022 (16-18) ROOM C	Gated Recurrent Networks I Deep learning for sequence processing; gradient issues;	[DL] Sections 10.1-10.3, 10.5-10.7, 10.10, 10.11	Additional Readings [29] Paper describing gradient vanish/explosion [30] Original LSTM paper [31] An historical view on gated RNN [32] Gated recurren units paper [33] Seminal paper on dropout regularization
  19	29/03/2022 (14-16)	Gated Recurrent Networks II long-short term memory; gated recurrent units; generative use of RNN		Sofware A fun and high-level introduction to generative use of LSTM The up-to-date implementation of NeuraTalk
  20	30/03/2022 (16-18)	Coding practice I - Tensorflow
  21	31/03/2022 (14-16)	Coding practice II - PyTorch
  22	05/04/2022 (14-16)	Deep Randomized Networks - Guest lecture by Claudio Gallicchio reservoir computing; randomized models; echo state networks
  23	06/04/2022 (16-18)	Advanced Recurrent Architectures and Attention sequence-to-sequence;  attention models; multiscale network; hierarchical models	[DL] Sections 10.12, 12.4.5	Additional Readings [34,35] Models of sequence-to-sequence and image-to-sequence transduction with attention [36,37] Models optimizing dynamic memory usage (clockwork RNN, zoneout) [38] Transformer networks: a paper on the power of attention without recurrence
  24	07/04/2022 (14-16)	Neural Reasoning memory networks; neural Turing machines		Additional Readings [39] Differentiable memory networks [40,41] Neural Turing Machines and follow-up paper on pondering networks
  25	12/04/2022 (14-16)	Unsupervised and Generative Deep Learning I explicit distribution models; neural ELBO; variational autoencoders	[DL] Sections 20.9, 20.10.1-20.10.3	Additional Readings [42] PixelCNN - Explict likelihood model [43] Tutorial on VAE Sofware Pixel-CNN code A tutorial on VAE with code
  26	13/04/2022 (16-18)	Unsupervised and Generative Deep Learning II generative adversarial networks; adversarial autoencoders	[DL] Section 20.10.4	Additional Readings [44] Tutorial on GAN (here another online resource with GAN tips) [45] Wasserstein GAN [46] Tutorial on sampling neural networks [47] Progressive GAN [48] Cycle Gan [49] Seminal paper on Adversarial AEs Sofware Official Wasserstein GAN code A (long) list of GAN models with (often) associated implementation

  
  

  • Lecture 15-16 slides File
  • Lecture 17 slides File
  • Lecture 18-19 slides File
  • Lecture 20 slides File
  • Lecture 21 slides File
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  • Lecture 24 slides File
  • Lecture 25 slides File
  • Lecture 26 slides File
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  Reinforcement Learning (14h)

  We formalise the reinforcement learning problem by rooting it into Markov decision processes and we provide an overview of the main approaches to design reinforcement learning agents, including model-based, model-free, value and policy learning. We link classical approaches with modern deep learning based approximators (deep reinforcement learning). We overview the main programming frameworks available. Methodologies covered include: dynamic programming, MC learning, TD learning, SARSA, Q-learning, deep Q-learning, policy gradient and deep policy gradient, MC tree search. 
  

  
  

  	Date	Topic	References	Additional Material
  28	21/04/2022 (14-16)	Reinforcement learning fundamentals reinforcement learning problems; environment; agent; actions and policies; taxonomy of approaches	[RL] Chapter 1	Software Open AI gym for RL environments and tasks
  29	26/04/2022 (14-16)	Markov Decision Processes formal model of RL probelms; rewards; returns; Bellman expectation and optimality	[RL] Chapter 3
  30	27/04/2022 (14-16)	Model-Based Planning dynamic programming; policy evaluation; policy iteration; value iteration	[RL] Chapter 4	Software Dynamic programming demo on Gridworld in Javascript (with code)
  31	28/04/2022 (14-16)	Model-free reinforcement learning model-free predition; model-free control; Monte Carlo methods; TD learning;  SARSA; Q-learning	[RL] Section 5.1-5.6, 6.1-6.6, 7.1, 7.2, 12.1, 12.2, 12.7	Additional reading: [50] The original Q-learning paper Software: TD learning demo on Gridworld in Javascript (with code) A Javascript demo environment based on AIXI models
  	03/05/2022 (14-16)	NO LECTURE
  	04/05/2022 (16-18)	NO LECTURE
  32	05/05/2022 (14-16)	Value-function Approximation linear incremental methods; batch value function approximation; deep Q-learning; linear least-squares control	[RL] Section 9.1-9.5, 9.8, 10.1, 11.1-11.5	Additional Reading: [51] Original DQN paper [52] Double Q-learning [53] Dueling Network Architectures [54] Prioritized Replay
  33	09/05/2022 (16-18 - ROOM C)	Policy gradient methods	[RL] Chapter 13	Additional Reading: [55] Original REINFORCE paper [56] Learning with the actor-critic architecture [57] Accessible reference to natural policy gradient [58] A3C paper [59] Deep Deterministic Policy Gradient [60] TRPO paper [61] PPO paper
  34	10/05/2022 (14-16)	Integrating Learning and Planning	[RL] Chapter 8, Sect 16.6	Additional Reading: [62] UCT paper: the introduction of Monte-Carlo planning [63] MoGo: the grandfather of AlphaGo (RL using offline and online experience) [64] AlphaGo paper [65] AlphaGo without human bootstrap

  
  

  • Lecture 28 slides File
  • Lecture 29 slides File
  • Lecture 30 slides File
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  • Lecture 32 slides File
  • Lecture 33 slides File
  • Lecture 34 slides File
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  Advanced Topics and Applications (8h)

  The module covers some recent and interesting development and research topics in the field of machine learning. Topics choice is likely to vary at each edition. Example topics include: deep learning for graphs, learning with structured data, continual learning, distributed learning, learning-reasoning integration, edgeAI,etc.. The module concludes with a final lecture which discusses the course content retrospectively and details the exam modalities, topics and deadlines.
  

  	Date	Topic	References	Additional Material
  27	20/04/2022 (16-18)	Continual Learning - Guest lecture by Vincenzo Lomonaco
  35	11/05/2022 (16-18)	Deep learning for graphs		Software - PyDGN: our in-house DLG library - PyTorch geometric - Deep graph library Additional readings [66-67] Seminal works on neural networks for graphs [68] Recent tutorial paper
  36	12/05/2022 (14-16)	Final lecture
  37	20/05/2022 (11-13) ROOM D1	Research seminars by Ph.D. students Draft programme: Andrea Cossu - The reasonable effectiveness of pre-trained models in Continual Learning Michele Resta - Continual Incremental Language Learning for Neural Machine Translation RIccardo Massidda - Ontology-Driven Semantic Alignment of Artificial Neurons Danilo Numeroso - Neural Algorithmic Reasoning Francesco Landolfi - Graph Pooling with Maximum Weight k-Independent Sets

  
  

  • Lecture 27 slides File
  • Lecture 35 slides File
  • Lecture 36 slides File
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  Course Grading and Exams

  Typical course examination (for students attending the lectures) is performed in 2 stages: midterm assignments and an oral exam. Midterms waive the final project.
  

  Midterm Assignment

  Midterms consist in short assignments involving with one of the following tasks:

  • A quick and dirty (but working) implementation of a simple pattern recognition algorithm
  • A report concerning the experience of installing and running a demo application realized using available deep learning and machine learning libraries
    
  • A summary of a recent research paper on topics/models related to the course content.
    

  The midterms can consist in either the delivery of code (e.g. colab notebook) or a short slide deck (no more than 10 slides) presenting the key/most-interesting aspects of the assignment. 
  

  Students might be given some amount of freedom in the choice of assignments, pending a reasonable choice of the topic. The assignments will roughly be scheduled every 3/4 weeks.

  Oral Exam

  The oral examination will test knowledge of the course contents (models, algorithms and applications).
  

  Exam Grading (with Midterms)
  

  The final exam vote is given by the oral grade. The midterms only wave the final project but do not contribute to the grade. In other words you can only fail or pass a midterm. You need to pass all midterms in order to succesfully wave the final project.
  

  Alternative Exam Modality (No Midterms / Non attending students)

  Working students, those not attending lectures, those who have failed midterms or simply do not wish to do them, can complete the course by delivering a final project and an oral exam.  Final project topics will be released in the final weeks of the course: contact the instructor by mail to arrange choice of the topics once these are published.
  

  The final project concerns preparing a report on a topic relevant to the course content or the realization of a software implementing a non-trivial learning model and/or a PR application relevant for the course. The content of the final project will be discussed in front of the instructor and anybody interested during the oral examination. Students are expected to prepare slides for a 15 minutes presentation which should summarize the ideas, models and results in the report. The exposition should demonstrate a solid understanding of the main ideas in the report.

  Grade for this exam modality is determined as
  

   \( G = 0.5 \cdot (G_P + G_O) \)

  where \( G_P \in [1,32] \) is the project grade and \( G_O \in [1,30] \) is the oral grade
  
• 

  Midterms and Projects

  • Midterm 1 (2022) Assignment

    Due: Friday, 18 March 2022, 12:00 PM
  • Midterm 2 (2022) Assignment

    Due: Thursday, 14 April 2022, 6:00 PM
  • Midterm 3 (2022) Assignment

    Opened: Thursday, 28 April 2022, 12:00 AM

    Due: Monday, 16 May 2022, 12:00 PM
  • Midterm 4 (2022) Assignment

    Opened: Monday, 16 May 2022, 12:00 AM

    Due: Tuesday, 31 May 2022, 2:00 PM
  • Final Projects (2022) Assignment
  • Final Project Delivery - Session 3 (Summer 2022) Assignment

    Opened: Monday, 16 May 2022, 12:00 AM

    Due: Tuesday, 31 May 2022, 2:00 PM
  • Final Project Delivery - Session 4 (Summer 2022) Assignment

    Opened: Monday, 16 May 2022, 12:00 AM

    Due: Tuesday, 21 June 2022, 2:00 PM
  • Final Project Delivery - Session 5 (Summer 2022) Assignment

    Opened: Monday, 16 May 2022, 12:00 AM

    Due: Friday, 8 July 2022, 2:00 PM
• 

  Bibliography

  
  

  Bibliographic References
  

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  2. Tinne Tuytelaars and Krystian Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, Vol. 3, No. 3 (2007) 177–2, Online Version
  3. C. Glymour, Kun Zhang and P. Spirtes, Review of Causal Discovery Methods Based on Graphical Models Front. Genet. 2019, Online version
     
  4. Bacciu, D., Etchells, T. A., Lisboa, P. J., & Whittaker, J. (2013). Efficient identification of independence networks using mutual information. Computational Statistics, 28(2), 621-646, Online version
     
  5. Tsamardinos, I., Brown, L.E. & Aliferis, C.F. The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65, 31–78 (2006), Online version
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  7. Charles Sutton and Andrew McCallum,  An Introduction to Conditional Random Fields, Arxiv
  8. Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction, Foundations and Trends in Computer Graphics and Vision, Online Version
  9. Philipp Krahenbuhl, Vladlen Koltun, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Proc.of NIPS 2011, Arxiv
  10. D. Blei, A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003
      
  11. D. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012, Free Online Version
      
  12. G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual Categorization with Bags of Keypoints. Workshop on Statistical Learning in Computer Vision. ECCV 2004, Free Online Version
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  14. Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Technical Report 2010-003, University of Toronto, 2010
  15. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel. Handwritten digit recognition with a back-propagation network, Advances in Neural Information Processing Systems, NIPS, 1989
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  28. G. Alain, Y. Bengio. What Regularized Auto-Encoders Learn from the Data-Generating Distribution, JMLR, 2014.
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  33. N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014
  34. Bahdanau et al, Neural machine translation by jointly learning to align and translate, ICLR 2015, Arxiv
      
  35. Xu et al, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, ICML 2015, Arxiv
  36. Koutník et al, A Clockwork RNN, ICML 2014, Arxiv
  37. Krueger, Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activation, ICLR 2018, Arxiv
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  43. C. Doersch, A Tutorial on Variational Autoencoders, 2016, Arxiv
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  45. Arjovsky et al, Wasserstein GAN, 2017, Arxiv
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  47. T. Karras et al, Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018, Arxiv
  48. Jun-Yan Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, ICCV 2017 Arxiv
  49. Alireza Makhzani et al, Adversarial Autoencoders, NIPS 2016, Arxiv
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  53. Wang et al, Dueling Network Architectures for Deep Reinforcement Learning, ICML, 2016, PDF
  54. Schaul et al, Prioritized Experience Replay, ICLR, 2016, PDF
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