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ML-IAP/CCA-2023

November 27, 2023 to December 1, 2023 Conference
Dual node
Europe/Paris timezone

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Session

Review presentations

Review
Nov 27, 2023, 8:00 PM
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA

Conveners

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations

  • There are no conveners in this block

Review presentations: Seminar at IAP:

  • Helena Domínguez Sánchez

Presentation materials

There are no materials yet.

  1. 135. TBD: Deep learning and numerical simulations
    Prof. Rupert Croft
    11/27/23, 8:00 PM
    New York
    Review
  2. 132. Generative models to assist sampling
    Prof. Marylou Gabrié (CMAP, Ecole Polytechnique)
    11/28/23, 10:00 AM
    Paris
    Review

    Deep generative models parametrize very flexible families of distributions able to fit complicated datasets of images or text. These models provide independent samples from complex high-distributions at negligible costs. On the other hand, sampling exactly a target distribution, such a Bayesian posterior or the Boltzmann distribution of a physical system, is typically challenging: either...

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  3. 141. TBD: Deep learning and numerical simulations (Replay)
    Prof. Rupert Croft
    11/28/23, 11:30 AM
    Paris
    Review
  4. 136. TBD: Domain adaptation
    David Shih (Rutgers University)
    11/28/23, 9:00 PM
    New York
    Review
  5. 139. Generative models to assist sampling
    Prof. Marylou Gabrié (CMAP, Ecole Polytechnique)
    11/28/23, 10:30 PM
    New York
    Review

    Deep generative models parametrize very flexible families of distributions able to fit complicated datasets of images or text. These models provide independent samples from complex high-distributions at negligible costs. On the other hand, sampling exactly a target distribution, such a Bayesian posterior or the Boltzmann distribution of a physical system, is typically challenging: either...

    Go to contribution page
  6. 133. TBD: Symbolic regression
    Miles Cranmer (Cambridge University)
    11/29/23, 10:00 AM
    Online
    Review
  7. 130. TBD: ML and Bayesian inference in cosmology
    Prof. Jens Jasche (Stockholm University)
    11/29/23, 11:30 AM
    Paris
    Review
  8. 137. TBD: ML and Bayesian inference in cosmology (Replay)
    Prof. Jens Jasche (Stockholm University)
    11/29/23, 9:00 PM
    New York
    Review
  9. 143. TBD: Symbolic regression (Replay)
    Miles Cranmer (Cambridge University)
    11/29/23, 10:30 PM
    New York
    Review
  10. 131. Capitalizing on Artificial Intelligence for LSS Cosmology
    Dr Tomasz Kacprzak (ETH Zurich/ Swiss Data Science Center)
    11/30/23, 10:00 AM
    Paris
    Review

    In this review talk, I will show how artificial intelligence can bring tangible benefits to cosmological analysis of large-scale structure.
    I will focus on how the use of AI in the framework of Simulations-Based Inference to achieve scientific objectives that would not be attainable with classical 2-pt function analyses. I will show three avenues where, in my opinion, AI can bring the most...

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  11. 140. TBD: Domain adaptation (Replay)
    David Shih (Rutgers University)
    11/30/23, 11:30 AM
    Paris
    Review
  12. 134. TBD: Symmetries in deep learning
    Soledad Villar
    11/30/23, 8:30 PM
    New York
    Review
  13. 142. Capitalizing on Artificial Intelligence for LSS Cosmology (replay)
    Prof. Tomasz Kacprzak (ETH Zurich/ Swiss Data Science Center)
    11/30/23, 10:00 PM
    New York
    Review

    In this review talk, I will show how artificial intelligence can bring tangible benefits to cosmological analysis of large-scale structure.
    I will focus on how the use of AI in the framework of Simulations-Based Inference to achieve scientific objectives that would not be attainable with classical 2-pt function analyses. I will show three avenues where, in my opinion, AI can bring the most...

    Go to contribution page
  14. 138. TBD: Symmetries in deep learning (Replay)
    Soledad Villar
    12/1/23, 10:00 AM
    Paris
    Review
  15. 146. Deep learning algorithms for morphological classification of galaxies
    Helena Domínguez Sánchez
    12/1/23, 11:15 AM
    Paris
    Review

    Galaxies exhibit a wide variety of morphologies which are strongly related to their star formation histories and formation channels. Having large samples of morphologically classified galaxies is fundamental to understand their evolution. In this talk, I will review my research related to the application of deep learning algorithms for morphological classification of galaxies. This technique...

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