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The course is held on the second term. The tentative schedule for A.A. 2022/23 is provided in table below.
The first lecture of the course will be ON FEBRUARY 21st 2023 h. 11.30 (note the slightly delayed starting time for only this lecture). 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 11.00-12.45 (Room C1, Online) Wednesday 16.15-18.00 (Room E, Online) Thursday 14.15-16.00 (Room C, Online)
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.
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, their combinaton 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, NTMs), generative deep learning (VAE, GANs, diffusion models, score-based models) 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 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 21/02/2023
Introduction to the course
Motivations and aim; course housekeeping (exams, timetable, materials); introduction to modern pattern recognition applications
Remember to fill in your contact details here (by 24/02)
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 22/02/2023
Timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.
Image Processing I
Spatial feature descriptors (color histograms, SIFT); spectral analysis.
 Survey on visual descriptors
- A tweakable and fast implementation of SIFT in C (on top of OpenCV)
Image Processing II
Feature detectors (edge, blobs); image segmentation; wavelet decompositions
 Survey on visual feature detectors
A reference book for the pattern recognition part is " S. THEODORIDIS, K. KOUTROUMBAS, Pattern Recognition, 4th edition". It is not needed for the sake of the course, but it is a reference book if you are interested on the topic. It is not available online for free (legally; what you do with Google is none of my business).
You can find the original NCUT paper freely available from authors here.
A wavelet browser to visualize some popular wavelet families and their instances, powered by the PyWavelet library.
- A tweakable and fast implementation of SIFT in C (on top of OpenCV)
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.Bayesian Learning III
sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methods
Date Topic References Additional Material 5 02/03/2023
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 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)
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)
 A short review of BN structure learning
 PC algorithm with consistent ordering for large scale data
 MMHC - Hybrid structure learning algorithm
If you are interested in deepening of your knowledge on causality this is an excellent book (also freely available online): Jonas Peters, Dominik Janzing, Bernhard Schölkopf, Elements of causal inference : foundations and learning algorithms, MIT Press.
- 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.
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)
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)
 A classical tutorial introduction to HMMs
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)
[7,8] Two comprehensive tutorials on CRF ( more introductory and  more focused on vision)
 A nice application of CRF to image segmentation
Check out pgmpy: it has Python notebooks to introduce to working with MRF/CRF
Bayesian Learning I
Principles of Bayesian learning; EM algorithm objective; principles of variational approximation
BRML] Sect. 11.2.1 (Variational EM) 12 16/03/2023
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
 LDA foundation paper
 A gentle introduction to latent topic models
 Foundations of bag of words image representation
Bayesian Learning III
sampling methods; ancestral sampling; Gibbs sampling and Monte Carlo methods
[BRML] Sect. 27.1-27.3, 27.4.1, 27.6.2 Additional Readings
 A step-by-step derivation of collapsed Gibbs sampling for LDA
bridging neural networks and generative models; stochastic neuron; restricted Boltzmann machine; contrastive divergence
[DL] Sections 20.1 and 20.2 Additional Readings
 A clean and clear introduction to RBM from its author
Matlab code for Deep Belief Networks (i.e. stacked RBM) and Deep Boltzmann Machines.
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/generative 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. diffusion models, score-based methods. Methodological lectures will be complemented by introductory seminars to Keras-TF and Pytorch.
Date Topic References Additional Material 15 23/03/2023
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.
Convolutional Neural Networks II
CNN architectures for image recognition; convolution visualization; advanced topics (deconvolution, dense nets); applications and code
[DL] Chapter 9 Additional Readings
 Complete summary of convolution arithmetics
 Seminal paper on batch normalization
 CNN interpretation using deconvolutions
 CNN interpretation with GradCAM
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
 DBN: the paper that started deep learning
 Deep Boltzmann machines paper
 Review paper on deep generative models
 Long review paper on autoencoders from the perspective of representation learning
 Paper discussing regularized autoencoder as approximations of likelihood gradient
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
 Paper describing gradient vanish/explosion
 Original LSTM paper
 An historical view on gated RNN
Coding practice I - PyTorch
Guest lecture by Danilo Numeroso
Coding practice I - Tensorflow
Guest lecture by Valerio De Caro
NO LECTURE: Easter Break 21 12/04/2023
Gated Recurrent Networks II
long-short term memory; gated recurrent units; generative use of RNN
 Gated recurren units paper
 Seminal paper on dropout regularization
Advanced Recurrent Architectures I
sequence-to-sequence; attention models;
[DL] Sections 10.12, 12.4.5 Additional Readings
[34,35] Models of sequence-to-sequence and image-to-sequence transduction with attention
 Transformer networks: a paper on the power of attention without recurrence
Advanced Recurrent Architectures II
multiscale network; hierarchical models; memory networks; neural Turing machines
[37,38] Models optimizing dynamic memory usage (clockwork RNN, zoneout)
 Differentiable memory networks
[40,41] Neural Turing Machines and follow-up paper on pondering networks
Guest lecture by Andrea Ceni
03-04/05/2023 NO LECTURE 29 09/05/2023
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
 PixelCNN - Explict likelihood model
 Tutorial on VAE
Unsupervised and Generative Deep Learning II
generative adversarial networks; adversarial autoencoders
[DL] Section 20.10.4 Additional Readings
 Tutorial on GAN (here another online resource with GAN tips)
 Wasserstein GAN
 Tutorial on sampling neural networks
 Progressive GAN
 Cycle Gan
 Seminal paper on Adversarial AEs
Unsupervised and Generative Deep Learning III
diffusion models; latent space diffusion; conditional diffusion models
 Introductory and survey paper on diffusion models Additional Readings
 Seminal paper introducing diffusion models
 An intepretation of diffusion models as score matching
 Paper introducing the diffusion model reparameterization
 Diffusion beats GAN paper
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). Methodologies covered include: dynamic programming, MC learning, TD learning, SARSA, Q-learning, deep Q-learning, policy gradient and deep policy gradient.
Date Topic References Additional Material 24 18/04/2023
Reinforcement learning fundamentals & Model-Based Planning
reinforcement learning problems; environment; agent; actions and policies; taxonomy of approaches; formal model of RL probelms; policy evaluation; policy iteration; value iteration
[RL] Chapter 1, Chapter 3, Chapter 4 Software
- Open AI gym for RL environments and tasks
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:
 The original Q-learning paper
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:
 Original DQN paper
 Double Q-learning
 Dueling Network Architectures
 Prioritized Replay
25-27/04/2023 NO LECTURES 27 28/04/2023
Policy gradient & Actor-Critic methods [RL] Chapter 13 Additional Reading:
 Original REINFORCE paper
 Learning with the actor-critic architecture
 Accessible reference to natural policy gradient
 A3C paper
 Deep Deterministic Policy Gradient
 TRPO paper
 PPO paper
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, 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 32 16/05/2023
Deep learning for graphs Software
- PyDGN: our in-house DLG library
- PyTorch geometric
- Deep graph library
[64-65] Seminal works on neural networks for graphs
 Recent tutorial paper
Algorithmic reasoning (Danilo Numeroso), continual learning (Andrea Cossu), graph reduction (Francesco Landolfi), causal learning (Riccardo Massidda), hyperparameters autotuning (Dario Balboni)
Typical course examination (for students attending the lectures) is performed in 2 stages: midterm assignments and an oral exam. Midterms waive the final project.
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
report concerning the experience of installing and running a demo
application realized using available deep learning and machine learning
- 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.
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
whereis the project grade and is the oral grade
Due: Thursday, 23 March 2023, 1:00 PM
Due: Thursday, 13 April 2023, 1:00 PM
Due: Friday, 12 May 2023, 6:00 PM
Opened: Tuesday, 16 May 2023, 11:15 AMDue: Tuesday, 20 June 2023, 9:00 AM
Opened: Sunday, 21 May 2023, 11:15 AMDue: Friday, 30 June 2023, 9:00 AM
Opened: Sunday, 21 May 2023, 11:15 AMDue: Tuesday, 20 June 2023, 2:00 PM
Opened: Sunday, 21 May 2023, 11:15 AMDue: Friday, 7 July 2023, 2:00 PM
Opened: Friday, 1 September 2023, 11:15 AMDue: Tuesday, 5 September 2023, 2:00 PM
Exam Session I - 30/05/2023 TIME PLACE NOTES 07/06/2023
Oral exam I 09/06/2023
Oral exam II Exam Session II - 20/06/2023 TIME PLACE NOTES 27/06/2023
Sala Polifunzionale - Dipartimento di informatica 28/06/2023
Sala Polifunzionale - Dipartimento di informatica 30/06/2023
Sala Riunioni Est - Dipartimento di informatica Exam Session III - 07/07/2023 TIME PLACE NOTES 17/07/2023
Sala Polifunzionale - Dipartimento di informatica 18/07/2023
Sala Polifunzionale - Dipartimento di informatica Exam Session IV - 06/09/2023 TIME PLACE NOTES 13/09/2023
Sala Seminari Est - Dipartimento di informatica 13/09/2023
Sala Seminari Est - Dipartimento di informatica
- Scott Krigg, Interest Point Detector and Feature Descriptor Survey, Computer Vision Metrics, pp 217-282, Open Access Chapter
- 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
- C. Glymour, Kun Zhang and P. Spirtes, Review of Causal Discovery Methods Based on Graphical Models Front. Genet. 2019, Online version
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
- 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
- Lawrence R. Rabiner:a tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, pages 257-286, Online Version
- Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields, Arxiv
- Sebastian Nowozin and Christoph H. Lampert, Structured Learning and Prediction, Foundations and Trends in Computer Graphics and Vision, Online Version
- Philipp Krahenbuhl, Vladlen Koltun, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials, Proc.of NIPS 2011, Arxiv
- D. Blei, A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003
- D. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012, Free Online Version
- 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
- W. M. Darling, A Theoretical and Practical Implementation Tutorial on Topic Modeling and Gibbs Sampling, Lecture notes
- Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Technical Report 2010-003, University of Toronto, 2010
- 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
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, NIPS, 2012
- S. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition, ICLR 2015, Free Online Version
- C. Szegedy et al, Going Deeper with Convolutions, CVPR 2015, Free Online Version
- K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. CVPR 2016, Free Online Version
- V. Dumoulin, F. Visin, A guide to convolution arithmetic for deep learning, Arxiv
- S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ICML 2013, Arxiv
- M.D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, ICML 2013, Arxiv
- J. Adebayo et al, Sanity Checks for Saliency Maps, NeurIPS, 2018
- G.E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks.Science 313.5786 (2006): 504-507, Free Online Version
- G.E. Hinton, R. R. Salakhutdinov. Deep Boltzmann Machines. AISTATS 2009, Free online version.
- R. R. Salakhutdinov. Learning Deep Generative Models, Annual Review of Statistics and Its Application, 2015, Free Online Version
- Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 35(8) (2013): 1798-1828, Arxiv.
- G. Alain, Y. Bengio. What Regularized Auto-Encoders Learn from the Data-Generating Distribution, JMLR, 2014.
- Y. Bengio, P. Simard and P. Frasconi, Learning long-term dependencies with gradient descent is difficult. TNN, 1994, Free Online Version
- S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation, 1997, Free Online Version
- K. Greff et al, LSTM: A Search Space Odyssey, TNNLS 2016, Arxiv
- C. Kyunghyun
et al, Learning Phrase Representations using RNN
Encoder-Decoder for Statistical Machine Translation, EMNLP 2014, Arxiv
- N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014
- Bahdanau et al, Neural machine translation
by jointly learning to align and translate, ICLR 2015, Arxiv
- Xu et al, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, ICML 2015, Arxiv
- A. Vaswan et al, Attention Is All You Need, NIPS 2017, Arxiv
- Koutník et al, A Clockwork RNN, ICML 2014, Arxiv
- Krueger, Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activation, ICLR 2018, Arxiv
- CJCH Watkins, P Dayan, Q-learning, Machine Learning, 1992, PDF
- Mnih et al,Human-level control through deep reinforcement learning, Nature, 2015, PDF
- van Hasselt et al, Deep Reinforcement Learning with Double Q-learning, AAAI, 2015, PDF
- Wang et al, Dueling Network Architectures for Deep Reinforcement Learning, ICML, 2016, PDF
- Schaul et al, Prioritized Experience Replay, ICLR, 2016, PDF
- Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine Learning, 1992, PDF
- Sutton et al, Policy gradient methods for reinforcement learning with function approximation, NIPS, 2000, PDF
- Peters & Schaal, Reinforcement learning of motor skills with policy gradients, Neural Networks, 2008, PDF
- Mnih et al, Asynchronous methods for deep reinforcement learning, ICLR, 2016, PDF
- Lillicrap et al., Continuous control with deep reinforcement learning, ICLR, 2016, PDF
- Schulman et al, Trust Region Policy Optimization, ICML, 2015, PDF
- Schulman et al, Proximal Policy Optimization Algorithms, Arxiv
- A. van der Oord et al., Pixel Recurrent Neural Networks, 2016, Arxiv
- C. Doersch, A Tutorial on Variational Autoencoders, 2016, Arxiv
- Ian Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks, 2016, Arxiv
- Arjovsky et al, Wasserstein GAN, 2017, Arxiv
- T. White, Sampling Generative Network, NIPS 2016, Arxiv
- T. Karras et al, Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018, Arxiv
- Jun-Yan Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, ICCV 2017 Arxiv
- Alireza Makhzani et al, Adversarial Autoencoders, NIPS 2016, Arxiv
- Ling Yang et al, Diffusion Models: A Comprehensive Survey of Methods and Applications, 2023, Arxiv
- Jascha Sohl-Dickstein et al, Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015, PDF
- Y. Song & S. Ermon, Generative Modeling by Estimating Gradients of the Data Distribution, NeurIPS 2019, PDF
- Jonathan Ho et al, Denoising Diffusion Probabilistic Models, NeurIPS 2020, Arxiv
- P. Dhariwal & A. Nichol, Diffusion Models Beat GANs on Image Synthesis, NeurIPS 2021, PDF
- A. Micheli, Neural Network for Graphs: A Contextual Constructive Approach. IEEE TNN, 2009, Online
- Scarselli et al, The graph neural network model, IEEE TNN, 2009, Online
- Bacciu et al, A Gentle Introduction to Deep Learning for Graphs, Neural Networks, 2020, Arxiv