Section outline

◄Assignments and exams
►Slides
    1. 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
    2. D. Blei, A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003
    3. D. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012, Free Online Version
    4. W. M. Darling, A Theoretical and Practical Implementation Tutorial on Topic Modeling and Gibbs Sampling, Lecture notes
    5. Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Technical Report 2010-003, University of Toronto, 2010
    6. 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
    7. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, NIPS, 2012
    8. S. Simonyan and A. Zisserman.  Very deep convolutional networks for large-scale image recognition, ICLR 2015, Free Online Version
    9. C. Szegedy et al,  Going Deeper with Convolutions, CVPR 2015, Free Online Version
    10. K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. CVPR 2016, Free Online Version
    11. V. Dumoulin, F. Visin, A guide to convolution arithmetic for deep learning, Arxiv
    12. S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ICML 2013,  Arxiv
    13. F. Yu et al, Multi-Scale Context Aggregation by Dilated Convolutions, ICLR 2016, Arxiv
    14. S. Ren et al, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NeurIPS 2015
    15. Y. Bengio, P. Simard and P. Frasconi, Learning long-term dependencies with gradient descent is difficult. TNN, 1994, Free Online Version
    16. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation, 1997, Free Online Version
    17. K. Greff et al, LSTM: A Search Space Odyssey, TNNLS 2016, Arxiv
    18. C. Kyunghyun et al, Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, EMNLP 2014, Arxiv
    19. N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014
    20. Bahdanau et al, Neural machine translation by jointly learning to align and translate, ICLR 2015, Arxiv
    21. Xu et al, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, ICML 2015, Arxiv
    22. A. Vaswan et al, Attention Is All You Need, NIPS 2017, Arxiv
    23. A. Dosovitskiy et al,  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ICLR 2021
    24. G.E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science 313.5786 (2006): 504-507, Free Online Version
    25. G.E. Hinton, R. R. Salakhutdinov. Deep Boltzmann Machines. AISTATS 2009, Free online version.
    26. R. R. Salakhutdinov. Learning Deep Generative Models, Annual Review of Statistics and Its Application, 2015, Free Online Version
    27. 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.
    28. C. Doersch, A Tutorial on Variational Autoencoders, 2016, Arxiv
    29. Ian Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks, 2016, Arxiv
    30. Arjovsky et al, Wasserstein GAN, 2017, Arxiv
    31. T. White, Sampling Generative Network, NIPS 2016, Arxiv
    32. T. Karras et al, Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018, Arxiv
    33. Jun-Yan Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, ICCV 2017 Arxiv
    34. Alireza Makhzani et al, Adversarial Autoencoders, NIPS 2016, Arxiv
    35. I. Kobyzev et al Normalizing Flows: An Introduction and Review of Current Methods, Arxiv
    36. L Dinh et al, Density Estimation using real NVP, ICLR 2017, PDF
    37. D. Kingma & P. Dhariwal, Glow: Generative flow with invertible 1x1 convolutions, NeurIPS 2018, PDF
    38. G. Papamakarios et al, Masked Autoregressive Flow for Density Estimation, NeurIPS 2017, PDF
    39. Ling Yang et al, Diffusion Models: A Comprehensive Survey of Methods and Applications, 2023, Arxiv
    40. Jascha Sohl-Dickstein et al, Deep Unsupervised Learning using Nonequilibrium Thermodynamics, ICML 2015, PDF
    41. Jonathan Ho et al, Denoising Diffusion Probabilistic Models, NeurIPS 2020, Arxiv
    42. P. Dhariwal & A. Nichol, Diffusion Models Beat GANs on Image Synthesis, NeurIPS 2021, PDF 
    43. Hyvärinen, Estimation of Non-Normalized Statistical Models by Score Matching, JMLR 2005, PDF
    44. Song, Ermon, Generative Modeling by Estimating Gradients of the Data Distribution, NeurIPS 2019, PDF
    45. Liu, Gong, Liu, Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow, ICLR, 2023, PDF
    46. A. Micheli, Neural Network for Graphs: A Contextual Constructive Approach. IEEE TNN, 2009, Online
    47. Scarselli et al, The graph neural network model, IEEE TNN, 2009, Online
    48. Bacciu et al, A Gentle Introduction to Deep Learning for Graphs, Neural Networks, 2020, Arxiv
    49. Bacciu et al,  Probabilistic Learning on Graphs via Contextual Architectures, 2020, JMLR
    50. Gravina et al, ANTI-SYMMETRIC DGN: A STABLE ARCHITECTURE FOR DEEP GRAPH NETWORKS, ICLR, 2023, Arxiv
    51. A. Gravina and D. Bacciu, Deep learning for dynamic graphs: models and benchmarks, 2024, TNNLS
    52. D. Numeroso et al, Dual Algorithmic Reasoning, ICRL, 2023, Arxiv
    53. L. Rampášek et al, Recipe for a General, Powerful, Scalable Graph Transformer, NeurIPS 2022, Arxiv
    54. CJCH Watkins, P Dayan, Q-learning, Machine Learning, 1992, PDF
    55. Mnih et al,Human-level control through deep reinforcement learning, Nature, 2015, PDF
    56. Sutton et al, Policy gradient methods for reinforcement learning with function approximation, NIPS, 2000, PDF
    57. Schulman et al, Trust Region Policy Optimization, ICML, 2015, PDF
◄Assignments and exams
►Slides