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	title={TrIK-SVM : more efficiency for learning in Kre\u{\i}n spaces. Application to non-convex combinations in MKL},
	author={Gaëlle Loosli},
	journal = {Poster at Women in Machine Learning (WiML) workshop, co-located with NeurIPS 2018},
	year = {2018},
  abstract={We propose an improvement over the algorithm named KSVM, which solves SVM with indefinite kernels.
    The new algorithm is referred as TrIK-SVM. Its main advantage compared to KSVM is to produce sparse solutions, similarly to SVM with posititive definite kernels.
  From this new algorithm, we derive an application to the case of multiple kernel learning.
  In the MKL setting, all candidate kernels has to be positive definite and they are gathered using a convex linear combiantion, such that the final kernel is also positive definite.
  Here we propose to take advantage of TrIK-SVM to propose a new MKL approach withou any positivity constraints: any symmmetric kernel linearly combined with positive or negative coefficients.}

  author = {Ismaïla Seck and Gaëlle Loosli},
  title = { Generative adversarial nets and Cerema AWP dataset},
  booktitle = {ENBIS},
  year = {2018},
  url = {https://www.enbis.org},
  abstract = {  This talk will be about Generative Adversarial Networks(GANs), and a recently introduced dataset, the Cerema AWP(Adverse Weather Pedestrian).
    We want to assess the capacity of GANs to generate a particular element, in our case a pedestrian, at a specified place.
    The cerema AWP database is a good database for that task since for each image we have the bounding box of the pedestrian.
     The Cerema AWP dataset is an image dataset that was produced in a special installation, a tunnel in which different weather condition can be artificially created.
      And since that database was originally created for pedestrian detection, there is on each image a pedestrian.
      And the dataset is annotated according to the weather (10 different weathers), the pedestrian (5 different), their clothes (each pedestrian appears with two different clothes).
      Additional information such as the pedestrian’s direction or the bounding box of the pedestrian is available.
      The controlled environment, and those detailed information make this database attractive for our purpose.
      Indeed the background being fixed, it seems to be a simpler version of the problem we would get with different backgrounds, perspectives or other uncontrolled variations.
       In the cerema AWP database, most of the variation being controlled and associated with labels, we can study the generation, with all the conditions or according to a subset of weather or other conditions.
       In a previous study using a standard GAN, generated images presented a mixture of weather on a single output, showing that the generative network had trouble matching the dataset distribution.
       This problem was solved using a conditioning on the weather. Now the generated images have a uniform weather but a problem persists: we don’t have pedestrians on images.
        We are going to present the architectures, the ways of conditioning and others tricks to help the generator focus on the generation of pedestrians while generating realistic images.}
  author = {Ismaïla Seck and Khouloud Dahmane and Pierre Duthon and Gaëlle Loosli},
  title = {Baselines and a datasheet for the Cerema AWP dataset},
  booktitle = {Conférence d'Apprentissage CAp},
  year = {2018},
  url = {http://cap2018.litislab.fr},
  abstract = {This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format.
     We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks.
     Moreover, we decided to follow the very recent suggestions of {\em datasheets for dataset}, trying to standardize all the available information of the dataset, with a transparency objective.},
  doi = {10.13140/RG.2.2.36360.93448}