:orphan: |rss_image| **article - 1/1** :ref:`Blog ` :ref:`article (8) ` :ref:`articles (3) ` :ref:`cours (6) ` :ref:`module (7) ` :ref:`paper (3) ` .. |rss_image| image:: feed-icon-16x16.png :target: ../_downloads/rss.xml :alt: RSS ---- .. index:: article .. _ap-cat-article-0: article - 1/1 +++++++++++++ .. blogpostagg:: :title: Un algorithme d'inspiration quantique :date: 2022-10-01 :keywords: algorithme :categories: article :rawfile: 2022/2022-10-01_quantique.rst Une petite introduction sur l'informatique quantique. ... .. blogpostagg:: :title: Introduction à la programmation (sketch) :date: 2021-09-08 :keywords: teachings :categories: article :rawfile: 2021/2021-09-08_introduction.rst **Présentation** * Programmation en classes préparatoires, `Informatique en CPGE `_ * architecture d'un ordinateur * base du langage python * arbre binaire, graphes * algorithme, parcours de graphe, distance d'édition, diviser pour régner, programmation dynamique * Langages haut niveau, bas niveau * C/C++ * Rust * Javascript, Python, matlab... * Python : machine learning * datascience, deep learning = python * automatisation, site web * Python populaire (travails, universités) * interfaçage avec C++ * Pratique * Trouver le plus court chemin d'une station de métro à une autre ? --> plus court chemin dans un graphe * Même question en tenant compte des temps de changement de ligne ? ... .. blogpostagg:: :title: Articles à lire, computing :date: 2021-09-02 :keywords: assurance :categories: article :rawfile: 2021/2021-09-02_reading.rst Accelerating deep learning (hardware) * `Google's AI Processor's (TPU) Heart Throbbing Inspiration `_ * `An in-depth look at Google's first Tensor Processing Unit (TPU) `_ Accelerating deep learning (algorithm) * `triton `_ * `deepspeed `_ .. blogpostagg:: :title: Articles à lire, graphes, distance :date: 2021-08-28 :keywords: assurance :categories: article :rawfile: 2021/2021-08-28_reading.rst Quelques liens autour des graphes, distances entre graphes. ... .. blogpostagg:: :title: Articles à lire - JMLR :date: 2021-07-29 :keywords: assurance :categories: article :rawfile: 2021/2021-07-29_reading.rst Assurance, ML * `Predicting Drought and Subsidence Risks in France `_ * `Collaborative Insurance Sustainability and Network Structure `_ * `Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning `_ * `A new GEE method to account for heteroscedasticity, using asymmetric least-square regressions `_ JMLR * `Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime `_ * `LassoNet: A Neural Network with Feature Sparsity `_ * `Bandit Convex Optimization in Non-stationary Environments `_ * `A General Framework for Adversarial Label Learning `_ * `Non-parametric Quantile Regression via the K-NN Fused Lasso `_ * `Towards a Unified Analysis of Random Fourier Features `_ * `Online stochastic gradient descent on non-convex losses from high-dimensional inference `_ * `Explaining Explanations: Axiomatic Feature Interactions for Deep Networks `_ * `LocalGAN: Modeling Local Distributions for Adversarial Response Generation `_ * `Prediction against a limited adversary `_ * `Sparse Tensor Additive Regression `_ * `RaSE: Random Subspace Ensemble Classification `_ * `Asynchronous Online Testing of Multiple Hypotheses `_ * `FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference `_ * `Single and Multiple Change-Point Detection with Differential Privacy `_ * `Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples `_ * `On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials `_ * `Consistent estimation of small masses in feature sampling `_ * `On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials `_ python * `river `_: online machine learning * `mvlearn `_ * `pykeen `_ * `POT `_ (Python Optimal Transfer) * `giotto-tda `_ (topological machine learning) * `Pykg2vec `_ .. blogpostagg:: :title: Article à lire, assurance :date: 2021-07-21 :keywords: assurance :categories: article :rawfile: 2021/2021-07-21_reading.rst * `Collaborative Insurance Sustainability and Network Structure `_ * `Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning `_ * `Estimating Inequality Measures from Quantile Data `_ * `Données Agrégées et Variables Compositionnelles : Note Méthodologique `_ .. blogpostagg:: :title: Article à lire - reinforcement learning :date: 2021-07-11 :keywords: apprentissage par renforcement :categories: article :rawfile: 2021/2021-07-11_reading.rst `Causal Reinforcement Learning using Observational and Interventional Data `_ .. blogpostagg:: :title: Prédire l'occupation des vélib :date: 2015-05-19 :keywords: velib,prédiction,article :categories: article,velib,machine learning :rawfile: 2015/2015-05-19_velib.rst C'est un article sur une façon d'exploiter les données d'un service de vélos dans une grande ville : `Predicting Occupancy Trends in Barcelona's Bicycle Service Stations Using Open Data `_. Modèle ARIMA et Random Forest. ---- |rss_image| **article - 1/1** :ref:`2022-10 (1) ` :ref:`2022-12 (2) ` :ref:`2023-01 (1) ` :ref:`2023-02 (1) ` :ref:`2023-04 (1) `