Temporada 2020/2021
Track on Machine Learning with Graphs (editions 2019 and 2021)
| Fecha | Aula | Hora | Ponente | Título | Material |
| 16/06/2021 | Online | 10:30-12:00 | — | 19. Applications of Graph NN | Video |
| 09/07/2021 | Online | 10:30-12:00 | — | 18. Limitations of Graph NNs | Video |
| 02/07/2021 | Online | 10:30-12:00 | — | 17. Reasoning over Knowledge Graphs | Video |
| 25/06/2021 | Online | 10:30-12:00 | — | 16. Network Evolution | Video |
| 18/06/2021 | Online | 10:30-12:00 | — | 15. Outbreak Detection in Networks | Video |
| 11/06/2021 | Online | 10:30-12:00 | — | 14. Influence Maximization in Networks | Video |
| 04/06/2021 | Online | 10:30-12:00 | — | 13. Probabilistic Contagion and Models of Influence | Video |
| 25/05/2021 | Online | 10:30-12:00 | — | 12. Network Effects and Cascading Behavior | Video + homework |
| 18/05/2021 | Online | 10:30-12:00 | — | 11. Link Analysis. PageRank | Video |
| 11/05/2021 | Online | 10:30-12:00 | — | 10. Deep Generative Models for Graphs | Video |
| 04/05/2021 | Online | 10:30-12:00 | — | 9. Graph NN Representations with Pythorch Geometric | Video, notebook |
| 27/04/2021 | Online | 10:30-12:00 | — | 8. Graph NN Representations | Video, nb2021 |
| 20/04/2021 | Online | 10:30-12:00 | — | 7. Graph Representation Learning | Video |
| 13/04/2021 | Online | 10:30-12:00 | — | 6. Message Passing and Node Classification | Video, homework |
| 06/04/2021 | Online | 10:30-12:00 | — | 5. Spectral Clustering | Video |
| 23/03/2021 | Online | 10:30-12:00 | — | 4. Community Structure in Networks | Video |
| 16/03/2021 | Online | 10:30-12:00 | — | 3. Motifs and Structural Roles In Networks | Video |
| 09/03/2021 | Online | 10:30-12:00 | — | Dockers | |
| 02/03/2021 | Online | 10:30-12:00 | — | 2. Properties of Networks and Random Graph Models | Video + homework |
| 23/02/2021 | Offline | 10:30-12:00 | — | 1. Introduction. Structure of Graphs | Video + homework |
Temporada 2018/2019
| Fecha | Aula | Hora | Ponente | Título | Material |
| 10/06/2019 | 4.2.E03 | 9:40 | Carlos Sevilla | Bayesian CCA | |
| 27/05/2019 | 42E02 | 9:40 | Simón Roca | Contextual String Embeddings for Sequence Labeling | URL |
| 29/04/2019 | 40B01A | 9:40 | Jesús Cid | PyTorch (6): Word-embeddings | |
| 01/03/2018 | 40B01A | 9:40 | Óscar García | PyTorch (5): RNN, LSTM. | |
| 18/03/2018 | 42E02 | 9:40 | Simón Roca | Reading Tea Leaves: How Humans Interpret Topic Models | Slides |
| 04/03/2019 | 42E02 | 9:40 | Lorena Álvarez | PyTorch (4): Image Style Transfer Using Convolutional Neural Networks | |
| 12/02/2019 | 41E02 | 9:40 | Carlos Sevilla | PyTorch (3): | |
| 05/02/2019 | 41E02 | 9:40 | Jesús Cid | Online Passive-Agressive Algorithms | |
| 22/01/2019 | 40E02 | 9:40 | Ángel Navia | PyTorch (2) (y II) | |
| 15/01/2019 | 40E02 | 9:40 | Jerónimo Arenas | PyTorch (2): | |
| 20/12/2018 | 42E00 | 9:40 | Luis Muñoz González | Adversarial Learning | Slides1, slides2 |
| 11/12/2018 | 41E02 | 9:40 | Óscar García | Generative Adversarial Nets | |
| 27/11/2018 | 41E02 | 9:40 | Manuel Vázquez | PyTorch (1): | |
| 20/11/2018 | 40E06 | 9:40 | Lorena Álvarez | Dropout: A Simple Way to Prevent Neural Networks from Overfitting | Slides |
Temporada 2017/2018
| Fecha | Aula | Hora | Ponente | Título | Material |
| 27/06/2018 | 4.0.D01 | 9:40 | Simón Roca | A Little Survey on Traditional and Current Word and Sentence Embeddings | Slides |
| 20/06/2018 | 4.0.E06 | 9:40 | Carlos Sevilla | Parsimonious CCA for Biomarker Design: Characterization of Mental Disorders | |
| 13/06/2018 | 4.0.E05 | 9:40 | Pablo Martínez | [DL] 20. Deep Generative Models | |
| 05/06/2018 | 7.2.J04 | 9:40 | Luis Azpicueta | Detección Automática de las Diferencias Perceptuales entre Respuestas Impulsivas de Recintos | |
| 28/05/2018 | 4.2.E02 | 9:40 | Ángel Navia | Privacy Preserving Machine Learning | |
| 21/05/2018 | 4.2.E02 | 9:40 | Jesús Cid | [DL] 19. Approximate Inference | Slides |
| 14/05/2018 | 4.2.E02 | 9:40 | Simón Roca | Unveiling Hidden Semantic Structures of Corpora Using Topic Models | |
| 07/05/2018 | 4.2.E02 | 9:40 | Jerónimo Arenas | [DL] 18. Confronting the Partition Function | Slides |
| 23/04/2018 | 4.2.E02 | 9:40 | Jesús Fernández Bes | Quantification of Physiological Cardiac Variability using Adaptive Signal Processing Techniques | |
| 16/04/2018 | 4.2.E02 | 9:40 | Adil Omari | Deep Residual Learning for Image Recognition | |
| 09/04/2018 | 4.2.E02 | 9:40 | Vanessa Gómez | [DL] 17. Monte Carlo Methods | |
| 19/03/2018 | 4.2.E02 | 9:40 | Óscar García | Worm-level Control through Search-based Reinforcement Learning | |
| 12/03/2018 | 4.2.E02 | 9:40 | Fernando de la Calle | Deep learning methods for Automatic Speech Recognition | arXiv, arXiv |
| 05/03/2018 | 4.2.E02 | 9:40 | Lorena Álvarez | [DL] 16. Structured Probabilistic Models for Deep Learning | Slides |
| 19/02/2018 | 4.2.E02 | 9:40 | Jesús Cid | Algorithms for recycling weakly labeled datasets | |
| 12/02/2018 | 4.2.E02 | 9:40 | Carlos Sevilla | [DL] 15. Representation Learning | |
| 05/02/2018 | 4.2.E02 | 9:40 | Pablo Martínez | [DL] 14. Autoencoders | |
| 01/02/2018 | 4.2.E02 | 9:40 | Luis Azpicueta | Head-Related Transfer Function (HRTF) + Machine Learning | |
| 22/01/2018 | 4.2.E02 | 9:40 | Manuel Vázquez | [DL] 13. Linear Factor Models | |
| 17/01/2018 | 4.1.E02 | 9:40 | Jerónimo Arenas | Random Walk Graph-based SemiSupervised Classification | Slides |
| 10/01/2018 | 4.1.E02 | 9:40 | Ángel Navia | [DL] 10. Sequence Modelling: Recurrent and Recursive Nets | NB, Slides |
| 29/11/2017 | 4.2.E02 | 9:40 | Vanessa Gómez | Bayesian Canonical Correlation Analysis | Notes |
| 22/11/2017 | 4.2.E02 | 9:40 | Simón Roca | [DL] 9. Convolutional Networks | |
| 15/11/2017 | 4.2.E02 | 9:40 | Lorena Álvarez | Semi-supervised learning | Slides |
| 08/11/2017 | 4.2.E02 | 9:40 | Adil Omari | [DL] 7. Regularization for Deep Learning and 8. Optimization for Training Deep Models | |
| 31/10/2017 | 4.0.E05 | 9:40 | Diego Rojo | Text Categorization from Category Names | |
| 18/10/2017 | 4.2.E02 | 9:40 | Óscar García | [DL] 6. Deep Feedforward Networks | |
| 20/09/2017 | 4.2.E02 | 9:40 | … | Preparatory session: content of the next talks. |
Temporada 2016/2017
| Fecha | Aula | Hora | Ponente | Título | Material |
| 19/07/2017 | 7.1.J03 | 9:30 | Jesús Cid-Sueiro | PSBI (4): Scaling Variational Mean Field Algorithms | … |
| 12/07/2017 | 4.2.E02 | 9:30 | Abdel Moujahid | The Metabolic Cost of Neuronal Activity | … |
| 04/07/2017 | 4.2.E02 | 9:30 | Pablo Martínez-Olmos | PSBI (3): Parallel and distributed MCMC | … |
| 29/06/2017 | 4.2.E02 | 9:30 | Manuel Vázquez-López | PSBI (2): MCMC with data subsets | Slides |
| 20/06/2017 | 4.2.E02 | 9:30 | Luis Azpicueta | Machine Learning Methods to Predict Diabetes Complications | … |
| 14/06/2017 | 4.2.E02 | 9:30 | Jerónimo Arenas-García | Patterns of Scalable Bayesian Inference (1): Background | Slides, Notes |
| 07/06/2017 | 7.1.J02 | 9:30 | Ángel Navia-Vázquez | Natural Language Processing | … |
| 31/05/2017 | 4.2.E02 | 9:30 | Vanessa Gómez-Verdejo | Building my Internet Search Engine: Scrapy and Lucene | Slides and code |
| 17/05/2017 | 4.2.E02 | 9:30 | Simón Roca-Sotelo | Topic Models and Word Embeddings | Slides |
| 10/05/2017 | 4.2.E02 | 9:30 | Lorena Álvarez Pérez | Training neural networks classifiers through Bayes risk minimization using Parzen windows | … |
| 03/05/2017 | 4.2.E02 | 9:30 | Adil Omari | Deep Neural Networks for Wind and Solar Energy Prediction | |
| 26/04/2017 | 4.2.E02 | 9:30 | Diego Rojo García | Visualización en Python con Bokeh | … |
| 19/04/2017 | 4.2.E02 | 9:30 | Jesús Cid-Sueiro | Convex Optimization (Cap 11: Interior Point Methods) | … |
| 05/04/2017 | 4.2.E02 | 9:30 | Óscar García-Hinde | Neo4j | Slides, Tutorial |
| 29/03/2017 | 4.2.E02 | 9:30 | Henry Navarro | Convex Optimization (Cap 10: Equality Constrained Minimization) | … |
| 22/03/2017 | 4.2.E02 | 9:30 | Jerónimo Arenas-García | SQL | ppt, csv … |
| 15/03/2017 | 4.2.E02 | 9:30 | Ángel Navia-Vázquez | Convex Optimization (Cap 9: Unconstrained Minimization) | … |
| 01/03/2017 | 4.2.E02 | 9:30 | Jesús Cid-Sueiro | Convex Optimization (Cap 8: Geometric Problems) | … |
| 22/02/2017 | 4.2.E02 | 9:30 | Vanessa Gómez-Verdejo | Spark with Dataframes | … |
| 15/02/2017 | 4.2.E02 | 9:30 | Luis Azpicueta | Google Fusion Tables | … |
| 08/02/2017 | 4.2.E02 | 9:30 | Simón Roca-Sotelo | Convex Optimization (Cap 7: Statistical Estimation) | … |
| 01/02/2017 | 4.2.E02 | 9:30 | Lorena Álvarez-Pérez | Qlik | … |
| 25/01/2017 | 4.2.E02 | 9:30 | Abdel Moujahid | (1) A Nonlinear Dynamic Approach to Mouse Behavior Classification, and (2) Object Detection based on Community Identification in Graphs | slides |
| 18/01/2017 | 4.0.E04 | 9:30 | Oscar García-Hinde | Convex Optimization (Cap 6: Approximation and Fitting) | … |
| 11/01/2017 | 4.0.E04 | 9:30 | Henry Navarro | Tableau | SW |
| 21/12/2016 | 4.2.E02 | 9:45 | Fernando García | Machine Learning en el Tratamiento de la Diabetes Tipo 1 | … |
| 14/12/2016 | 4.0.D01 | 12:00 | Miguel Lázaro | Hierarchical Compositional Feature Learning | … |
| 12/12/2016 | 4.0.D01 | 12:00 | Darío García | ML & AI @ FB | … |
| 23/11/2016 | 4.2.E02 | 9:30 | Jerónimo Arenas-García | Convex Optimization (Cap 5: Duality (II)) | … |
| 16/11/2016 | 4.2.E03 | 9:30 | Manuel Vázquez-López | Convex Optimization (Cap 5: Duality (I)) | … |
| 09/11/2016 | 4.2.E02 | 9:30 | Ángel Navia-Vázquez | Carto | … |
| 02/11/2016 | 4.2.E02 | 9:30 | Sergio Muñoz-Romero | Convex Optimization (Cap 4: Convex Optimization Problems) | … |
| 26/10/2016 | 4.2.E02 | 9:30 | Jesús Cid-Sueiro | Gephi | Tutorial |
| 19/10/2016 | 4.2.E02 | 9:30 | Luis Azpicueta | Convex Optimization (Cap. 3: Convex Functions) | … |
| 07/10/2016 | 4.2.E03 | 9:30 | Vanessa Gómez Verdejo | Convex Optimization (Cap. 2: Convex Sets) | … |
Temporada 2015/2016
| Fecha | Aula | Hora | Ponente | Título | Material |
| 21/06/2016 | 4.1.E02 | 11:00 | Juan José Choquehuanca | Generative Adversarial Nets | |
| 14/06/2016 | 4.1.E02 | 11:00 | Óscar García-Hinde | Consistent Algorithms for Clustering Time Series | |
| 07/06/2016 | 7.2.H01 | 9:30 | Henry Navarro | Cross Collection Topic Models | |
| 31/05/2016 | 4.2.E02 | 9:30 | Raúl Moreno-Salinas | An Introduction to Symbolic Regression | … |
| 17/05/2016 | 4.2.E03 | 9:40 | Jerónimo Arenas-García | Gaussian Processes for Big Data | |
| 10/05/2016 | 4.2.E03 | 9:30 | Ángel Navia-Vázquez | Overview of Apache Flink: Next-Gen Big Data Analytics Framework | … |
| 03/05/2016 | 4.2.E03 | 9:30 | Sergio Muñoz-Romero | Towards a neocortex-inspired machine learning | pdf, html |
| 19/04/2016 | 4.2.E03 | 9:30 | Jesús Cid-Sueiro | A survey on Sentiment Analysis | |
| 12/04/2016 | 4.2.E03 | 9:30 | Luis Azpicueta | Noise pollution predition: noise maps + machine learning | |
| 05/04/2016 | 4.2.E03 | 9:30 | Vanessa Gómez-Verdejo | A Survey on Transfer Learning | |
| 08/03/2016 | 4.2.E03 | 9:30 | Juan José Choquehuanca | Spectral Representations for Convolutional Neural Nets | NIPS2015 |
| 01/03/2016 | 4.2.E03 | 9:30 | Oscar García-Hinde | Feature Selection in Solar Radiation Prediction Using Bootstrapped SVRs | |
| 23/02/2016 | 4.2.E03 | 9:30 | Henry Navarro | The Tie Decay Problem in Social Networks: Why Do People Stop Calling? | … |
| 16/02/2016 | 4.2.E03 | 9:30 | Raúl Moreno-Salinas | Disruption Prediction in Nuclear Fusion Devices | Art1 , Art2 , Art3 , Thesis |
| 09/02/2016 | 4.2.E03 | 9:30 | Jesús Fernández-Bes | Introduction to Convolutional Networks using TensorFlow | web |
| 01/02/2016 | 4.0.E02 | 12:00 | Jerónimo Arenas-García | BDAS (Lab4): Machine Learning with Apache Spark | … |
| 20/01/2016 | 4.2.E03 | 9:30 | Ángel Navia-Vázquez | BDAS (L7+L8+Lab3): Data Quality, Exploratory Analysis and Machine Learning (y 2) | … |
| 13/01/2015 | 4.0.E02 | 9:30 | Ángel Navia-Vázquez | BDAS (L7+L8+Lab3): Data Quality, Exploratory Analysis and Machine Learning | … |
| 16/12/2015 | 4.0.E02 | 9:30 | Sergio Muñoz-Romero | BDAS (L5+L6+Lab2): Data Management | PySpark pictures |
| 09/12/2015 | 4.0.E02 | 9:30 | Jesús Cid-Sueiro | BDAS (Lab1): Introduction to Apache Spark | … |
| 02/12/2015 | 4.0.E02 | 9:30 | Luis Azpicueta | BDAS (L3+L4): Introduction to Apache Spark | … |
| 25/11/2015 | 4.0.E02 | 9:30 | Rocío Arroyo-Valles | Big Data with Apache Spark (L1+L2): Introduction and SW setup | Slides |
| 18/11/2015 | 4.0.E02 | 9:30 | Vanessa Gómez Verdejo | Hadoop and Map Reduce for Dummies | … |
Temporada 2014/2015
| Fecha | Aula | Hora | Ponente | Título | Material |
| 06/07/2015 | 4.2.E03 | 11:00 | Oscar García Hinde | A.Nguyen, J. Yosinski, J. Clune, “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images” | |
| 22/06/2015 | 4.2.E03 | 11:00 | Jerónimo Arenas-García | Collapsed Gibbs Sampling for Latent Dirichlet Allocation on Spark | |
| 19/06/2015 | 4.0.E04 | 12:00 | Miguel Lázaro Gredilla | Towards Human-Level AI | Vicarious |
| 10/06/2015 | 7.1.H01 | 10:00 | Jesús Fernández-Bes | Algorithms for Energy-Efficient Adaptive Wireless Sensor Networks | Tesis |
| 01/06/2015 | 4.2.E03 | 12:00 | Mrityunjoy Chakraborty | Sparse Distributed Estimation via Heterogeneous Diffusion Adaptive Networks | … |
| 25/05/2015 | 4.2.E03 | 11:00 | Ángel Navia-Vázquez | Procesado en array: núcleos y aproximación geométrica | doi1, doi2, tutorial1, tutorial2, |
| 11/05/2015 | 4.2.E03 | 11:00 | Sergio Muñoz-Romero | Análisis multivariante: soluciones eficientes e interpretables | Tesis |
| 27/04/2015 | 4.2.E03 | 11:00 | Jesús Cid-Sueiro | The U/P assignment problem for lifting transforms on graphs | … |
| 23/04/2015 | 4.2.E02 | 10:30 | Vanessa Gómez-Verdejo | * Model Predictive Regulation | |
| 13/04/2015 | 4.2.E03 | 11:00 | Luis Azpicueta | * Battery Storage System sizing in distribution feeders with distributed photovoltaic systems | doi |
| 23/03/2015 | 4.0.E05 | 11:00 | Sergio Muñoz-Romero | * Economic viability of energy storage systems based on price arbitrage potential in real-time U.S. electricity markets | doi |
| 16/03/2015 | 4.0.E05 | 11:00 | Jesús Fernández-Bes | * Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application | doi |
| 11/03/2015 | 2.1.C08 | 11:00 | Rocío Arroyo-Valles | * Stochastic Optimal Control of the Storage System to Limit Ramp Rates of Wind Power Output | doi |
| 03/03/2015 | 4.0.E04 | 14:00 | Oscar García Hinde | * Analysis of battery storage utilization for load shifting and peak smoothing on a distribution feeder in New Mexico | doi |
* Las sesiones del Smart-Grid track se han organizado de acuerdo con el documento de referencia elaborado por Manel y Vanessa, que podéis encontrar aquí
Pasadas ediciones
Participantes:
Son o han sido participantes regulares de este foro en algún momento:
- Oscar García
- Emilio Parrado
- Jesús Fernández
- Vanessa Gómez
- Jesús Cid
- Angel Navia
- Jerónimo Arenas
- Sergio Muñoz
- Luis A. Azpicueta
- Rocío Arroyo
- Miguel Lázaro
- Manel Martínez
- Harold Molina
- Saúl Blanco
- Bijit Kumar
- Emilio Ortiz
- Roberto Díaz
- Luis Muñoz
- Francisco Valverde