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Deep Learning: a brief overview on the possibility for astrophysics Fran cois-Xavier Dup e (LIS/Aix-Marseille Universit e, France) Journ ee SKA @ LAM Introduction What is Machine Learning? The aim of Machine Learning is to build a

  1. Deep Learning: a brief overview on the possibility for astrophysics Fran¸ cois-Xavier Dup´ e (LIS/Aix-Marseille Universit´ e, France) Journ´ ee SKA @ LAM

  2. Introduction What is Machine Learning? The aim of Machine Learning is to build a mathematical function which solve a human task. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 2 / 25

  3. Introduction What is Machine Learning? The aim of Machine Learning is to build a mathematical function which solve a human task. Today tasks include classification/regression; representation (or feature) learning; transfert learning; reinforcement learning; . . . F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 2 / 25

  4. Introduction Some history about Machine Learning 1956: the Dartmouth workshop Proposal We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 3 / 25

  5. Introduction Some history about Machine Learning (2) F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 4 / 25

  6. Introduction A brief timeline from Andrew L. Beam F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 5 / 25

  7. Introduction Today’s talk 1 Deep learning 2 Interactions with astrophysics 3 What next? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 6 / 25

  8. Deep learning Today’s talk 1 Deep learning 2 Interactions with astrophysics 3 What next? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 7 / 25

  9. Deep learning What is it? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 8 / 25

  10. Deep learning What is it? Deep learning ⇒ hierarchical learning with high order features. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 8 / 25

  11. Deep learning The location of deep learning From https://www.machinecurve.com/index.php/2017/09/30/ the-differences-between-artificial-intelligence-machine-learning-more/ F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 9 / 25

  12. Deep learning The zoo From http://www.asimovinstitute.org/neural-network-zoo/ F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 10 / 25

  13. Deep learning AlexNet ImageNet Classification with Deep Convolutional Neural Networks by A. Krizhevsky et al (NIPS 2012) F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 11 / 25

  14. Deep learning AlexNet (results) F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 12 / 25

  15. Deep learning Long Short-Time Memory (LSTM) Example of a recurrent neural network. From http://colah.github.io/posts/2015-08-Understanding-LSTMs/ F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 13 / 25

  16. Deep learning Long Short-Time Memory (LSTM): details Input gate : encode the input data. Output gate : create the next output (from the input). Forget gate : remove information from input data. Block input : output data from another block. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 14 / 25

  17. Deep learning Generative Adversarial Networks (GAN) Idea: building a generator that can fool a discriminator Generator : a NN that produce new data from noise . Discriminator : a classifier which distinguish fake data from true. A set of real samples. Generative adversarial nets by I. Goodfellow et al (NIPS 2014) F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 15 / 25

  18. Deep learning Generative Adversarial Networks (GAN): some results From https://adeshpande3.github.io/Deep-Learning-Research-Review-Week-1-Generative-Adversarial-Nets F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 16 / 25

  19. Interactions with astrophysics Today’s talk 1 Deep learning 2 Interactions with astrophysics 3 What next? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 17 / 25

  20. Interactions with astrophysics Some examples Star-galaxy Classification Using Deep Convolutional Neural Networks by Edward J. Kim Robert J. Brunner (MNRAS 2016) Idea: Use ConvNet on the reduced calibrated pixel values. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 18 / 25

  21. Interactions with astrophysics Some examples Star-galaxy Classification Using Deep Convolutional Neural Networks by Edward J. Kim Robert J. Brunner (MNRAS 2016) Idea: Use ConvNet on the reduced calibrated pixel values. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 18 / 25

  22. Interactions with astrophysics Some examples (2) Fast Cosmic Web Simulations with Generative Adversarial Networks by A.C. Rodr´ ıguez et al ( arXiv:1801.09070 ) Idea: use deep learning to avoid the computational cost of N-body simulation. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 19 / 25

  23. Interactions with astrophysics Some examples (2) Fast Cosmic Web Simulations with Generative Adversarial Networks by A.C. Rodr´ ıguez et al ( arXiv:1801.09070 ) Idea: use deep learning to avoid the computational cost of N-body simulation. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 19 / 25

  24. Interactions with astrophysics Some other examples from arXiv Deep learning for studies of galaxy morphology ( arXiv:1701.05917 ) Detecting Solar-like Oscillations in Red Giants with Deep Learning ( arXiv:1804.07495 ) Lunar Crater Identification via Deep Learning ( arXiv:1803.02192 ) Deep learning from 21-cm images of the Cosmic Dawn ( arXiv:1805.02699 ) Fast Point Spread Function Modeling with Deep Learning ( arXiv:1801.07615 ) F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 20 / 25

  25. What next? Today’s talk 1 Deep learning 2 Interactions with astrophysics 3 What next? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 21 / 25

  26. What next? Explainable Machine Learning Deep learning a black-box? Source: Department of Defense, Advanced Research Projects Agency. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 22 / 25

  27. What next? Evaluation One of the open question in Machine Learning: how to fully evaluate a method? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 23 / 25

  28. What next? Evaluation One of the open question in Machine Learning: how to fully evaluate a method? No free lunch theorem (Wolpert and Macready 1997) which ”states that any two optimization algorithms are equivalent when their performance is averaged across all possible problems”. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 23 / 25

  29. What next? Evaluation One of the open question in Machine Learning: how to fully evaluate a method? No free lunch theorem (Wolpert and Macready 1997) which ”states that any two optimization algorithms are equivalent when their performance is averaged across all possible problems”. Ugly Duckling theorem (Watanabe 1969) which states that perfect classification is impossible without some sort of bias. By OswaldLR - From: A Corny Concerto (2).png, Public Domain F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 23 / 25

  30. Conclusion Take away message Evaluation is primordial! Must be coherent with the task. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 24 / 25

  31. Conclusion Take away message Evaluation is primordial! Must be coherent with the task. Learning representation is a way to avoid some bias. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 24 / 25

  32. Conclusion Take away message Evaluation is primordial! Must be coherent with the task. Learning representation is a way to avoid some bias. Deep learning asks for big datasets, but scale very well. F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 24 / 25

  33. Conclusion Take away message Evaluation is primordial! Must be coherent with the task. Learning representation is a way to avoid some bias. Deep learning asks for big datasets, but scale very well. There is no free lunch! F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 24 / 25

  34. Conclusion Thank you for your attention. Any questions? F.-X. Dup´ e (AMU) Deep learning: an overview 16 May 2018 25 / 25

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