Showing 1–12 of 12 results for author: Guyon, I

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  1. arXiv:1805.02608  [pdf, other physics.soc-ph

    Anticipating contingengies in power grids using fast neural net screening

    Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici

    Abstract: We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic "N-1" reliability criterion, namely anticipating exceeding of thermal limit for any e… ▽ More

    Submitted 3 May, 2018; originally announced May 2018.

    Comments: IEEE WCCI 2018, Jul 2018, Rio de Janeiro, Brazil. 2018

  2. arXiv:1805.01174  [pdf, other stat.ML

    Optimization of computational budget for power system risk assessment

    Authors: Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick Panciatici

    Abstract: We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic risk-based security criterion. However, these approaches suffer fro… ▽ More

    Submitted 3 May, 2018; originally announced May 2018.

    Journal ref: 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, Oct 2018, Sarajevo, Bosnia and Herzegovina. 2018

  3. arXiv:1804.08046  [pdf, other cs.CV

    First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis

    Authors: Julio C. S. Jacques Junior, Yağmur Güçlütürk, Marc Pérez, Umut Güçlü, Carlos Andujar, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon, Marcel A. J. van Gerven, Rob van Lier, Sergio Escalera

    Abstract: Personality analysis has been widely studied in psychology, neuropsychology, signal processing fields, among others. From the computing point of view, by far speech and text have been the most analyzed cues of information for analyzing personality. However, recently there has been an increasing interest form the computer vision community in analyzing personality starting from visual information. R… ▽ More

    Submitted 21 April, 2018; originally announced April 2018.

    Comments: submitted to IEEE Transactions on Affective Computing

  4. arXiv:1803.04929  [pdf, other stat.ML

    SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning

    Authors: Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

    Abstract: We present the Structural Agnostic Model (SAM), a framework to estimate end-to-end non-acyclic causal graphs from observational data. In a nutshell, SAM implements an adversarial game in which a separate model generates each variable, given real values from all others. In tandem, a discriminator attempts to distinguish between the joint distributions of real and generated samples. Finally, a spars… ▽ More

    Submitted 13 March, 2018; originally announced March 2018.

  5. arXiv:1802.00745  [pdf, other cs.CV

    Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos

    Authors: Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yagmur Gucluturk, Umut Guclu, Xavier Baro, Isabelle Guyon, Julio Jacques Junior, Meysam Madadi, Stephane Ayache, Evelyne Viegas, Furkan Gurpinar, Achmadnoer Sukma Wicaksana, Cynthia C. S. Liem, Marcel A. J. van Gerven, Rob van Lier

    Abstract: Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in… ▽ More

    Submitted 2 February, 2018; originally announced February 2018.

    Comments: Preprint submitted to IJCV

  6. arXiv:1801.09870  [pdf, other stat.ML

    Fast Power system security analysis with Guided Dropout

    Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Antoine Marot, Patrick Panciatici

    Abstract: We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers. We use a deep feed-forward neural network trained with load-flows precomputed by simulation. Our architecture permits to train a network on so-called "n-1" problems, in whic… ▽ More

    Submitted 30 January, 2018; originally announced January 2018.

    Comments: European Symposium on Artificial Neural Networks, Apr 2018, Bruges, Belgium

  7. arXiv:1711.08936  [pdf, other stat.ML

    Causal Generative Neural Networks

    Authors: Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

    Abstract: We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data. CGNNs leverage conditional independencies and distributional asymmetries to discover bivariate and multivariate causal structures. CGNNs make no assumption regarding the lack of confounders, and learn a differentiable generative model of the data by using backpropagation. Extensive exper… ▽ More

    Submitted 5 February, 2018; v1 submitted 24 November, 2017; originally announced November 2017.

  8. arXiv:1709.09527  [pdf, ps, other stat.ML

    Introducing machine learning for power system operation support

    Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Patrick Panciatici, Antoine Marot

    Abstract: We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production system, including conventional power plants, less predictable re… ▽ More

    Submitted 27 September, 2017; originally announced September 2017.

    Comments: IREP Symposium, Aug 2017, Espinho, Portugal. 2017, \&\#x3008;http://irep2017.inesctec.pt/\&\#x3009

  9. arXiv:1709.05321  [pdf, other stat.ML

    Learning Functional Causal Models with Generative Neural Networks

    Authors: Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, David Lopez-Paz, Isabelle Guyon, Michèle Sebag, Aris Tritas, Paola Tubaro

    Abstract: We introduce a new approach to functional causal modeling from observational data. The approach, called Causal Generative Neural Networks (CGNN), leverages the power of neural networks to learn a generative model of the joint distribution of the observed variables, by minimizing the Maximum Mean Discrepancy between generated and observed data. An approximate learning criterion is proposed to scale… ▽ More

    Submitted 4 October, 2017; v1 submitted 15 September, 2017; originally announced September 2017.

  10. arXiv:1708.09794  [pdf, other cs.DL

    Design and Analysis of the NIPS 2016 Review Process

    Authors: Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg

    Abstract: Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. The massive scale as we… ▽ More

    Submitted 23 April, 2018; v1 submitted 31 August, 2017; originally announced August 2017.

  11. arXiv:1701.02664  [pdf, other cs.CV

    ChaLearn Looking at People: A Review of Events and Resources

    Authors: Sergio Escalera, Xavier Baró, Hugo Jair Escalante, Isabelle Guyon

    Abstract: This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challe… ▽ More

    Submitted 15 February, 2017; v1 submitted 10 January, 2017; originally announced January 2017.

    Comments: Paper to appear in proceedings of IJCNN 2017 - IEEE - Associated website: http://chalearnlap.cvc.uab.es

  12. arXiv:1310.4822  [pdf, ps, other cs.CV

    Principal motion components for gesture recognition using a single-example

    Authors: Hugo Jair Escalante, Isabelle Guyon, Vassilis Athitsos, Pat Jangyodsuk, Jun Wan

    Abstract: This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training-video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video… ▽ More

    Submitted 31 January, 2014; v1 submitted 17 October, 2013; originally announced October 2013.

    MSC Class: 68T45