.. blogpost::
    :title: Forward, forward
    :keywords: apprentissage, optimisation, gradient
    :date: 2023-01-11
    :categories: paper

    Un article intéressant :

    * `The Forward-Forward Algorithm: Some Preliminary Investigations
      <https://www.cs.toronto.edu/~hinton/FFA13.pdf>`_
    * `Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
      <https://proceedings.mlr.press/v9/gutmann10a.html>`_
    * `Training language models to follow instructions with human feedback
      <https://arxiv.org/abs/2203.02155>`_
    * `Attention Is All You Need
      <https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf>`_
    * `Optimal Transport for Counterfactual Estimation: A Method for Causal Inference
      <https://arxiv.org/abs/2301.07755>`_

    Et d'autres papiers :

    * `A Succinct Summary of Reinforcement Learning
      <https://arxiv.org/abs/2301.01379>`_
    * `Top ML Papers of the Week (Jan 1-8)
      <https://github.com/dair-ai/ML-Papers-of-the-Week>`_
    * `sparseml <https://github.com/neuralmagic/sparseml>`_
    * `solo-learn <https://github.com/vturrisi/solo-learn>`_
    * `deepchecks <https://github.com/deepchecks/deepchecks>`_