MLclub.net
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Organizers:
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Meeting Logistics:
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Academic year 2021-2022
2021-11-03 | Debate: ML & Exoplanets Panel:
Moderator: J. X. Prochaska [] Note: Because the U.S. has not shifted their clocks yet, this meeting will be at 5 PM (17:00) CET Zoom: https://ucsc.zoom.us/j/98363239395?pwd=akhPVnA3T3RwUEZaQVFIaEZWbEUyUT09 |
Academic year 2020-2021
2021-05-26 | Debate: ML & stellar spectroscopy Panel:
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2021-05-12 | Debate: ML Uncertainty Quantification for Scientific Applications Panel:
Moderator: F. Lanusse [Recording] | ||||||||||
2021-04-28 | Debate: The ML impact in cosmology
Moderator: B. Ménard [Recording] | ||||||||||
2021-04-14 | Debate: Outliers: How do we discover them with ML and what are they good for? Panel:
Moderator: X. Prochaska [Recording] | ||||||||||
2021-03-31 | Debate: How to study galaxy morphology? Panel:
Moderator: M. Huertas-Company [Recording] | ||||||||||
2021-03-17 | Debate: Will ML solve Photometric Redshifts? Panel:
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2021-03-03 | Debate: How should ML penetrate the natural sciences? Do we need ML institutes? Panel:
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2021-02-03 | Debate: What will ML do (or not) for the Rubin Observatory project (LSST)? Panel:
Moderator: B. Ménard, Recording | ||||||||||
2021-01-20 | Self-Supervised Representation Learning for Astronomical Images Slides Presentation by George Stein, UC Berkeley |
Academic year 2020-2021
2020-12-16 | Debate: What is Deep Learning Teaching Astronomy? Moderator: Alexie Leauthaud Panel: The ML Club organizers |
2020-12-02 | Siamese Neural Networks Learn Symmetry Invariants and Conserved Quantities Speaker: Sebastian Wetzel (Perimeter Institute for Theoretical Physics) |
2020-11-18 | Denoising Score Matching for Uncertainty Quantification in Inverse Problems: Application to gravitational lensing and Magnetic Resonance Imaging Speakers: Benjamin Remy, Zaccharie Ramzi (CEA Paris-Saclay) |
2020-10-21 | Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning | arXiv paper Speaker: Biwei Dai (Berkeley) |
2020-10-07 | A new vocabulary for patterns | arXiv paper Speaker: Sihao Cheng (Johns Hopkins Univ.) Beyond the Hubble Sequence - Exploring galaxy morphology with unsupervised machine learning | arXiv paper Speaker: Ting-Yun Cheng (Nottingham) |
2020-09-23 | Neural Scaling Laws and GPT-3 Speaker: Jared Kaplan (Johns Hopkins / OpenAI) Paper: https://arxiv.org/abs/2005.14165 Lecture notes on Machine Learning for Physicists |
2020-09-09 | Feature Extraction on Synthetic Black Hole Images Speaker: Joshua Yao-Yu Lin (U. Illinois) Paper: https://arxiv.org/abs/2007.00794 Anomaly Detection in Hyper Suprime-Cam Images with Generative Adversarial Networks Speaker: Kate Storey-Fisher (NYU) |
2020-08-26 | Discovering Symbolic Models from Deep Learning with Inductive Biases |
Academic year 2019-2020
2020-07-29 | Holiday break |
2020-07-15 | Flows for simultaneous manifold learning and density estimation #notagan Speaker: Johann Brehmer (NYU; https://johannbrehmer.github.io/) This work is based primarily on this paper: https://arxiv.org/abs/2003.13913 |
2020-07-01 | Learning maths from example with deep language models Speakers: François Charton (FAIR), Amaury Hayat (Paristech, Rutgers) https://arxiv.org/pdf/1912.01412.pdf , https://arxiv.org/pdf/2006.06462.pdf |
2020-06-03 | Speaker: J. Xavier Prochaska (AAII) |
2020-05-20 | SimCLR: A Simple Framework for Contrastive Learning of Visual Representations Speaker: Ting Chen (Google Research) |
2020-05-06 | Quasar continua predictions with neural spline flows Speaker: David Reiman (UCSC) |
2020-04-08 | Neural Networks with Euclidean Symmetry for Physical Sciences |
2020-03-11 | Statistical inference of dark matter substructure from strong gravitational lenses without a likelihood Speaker: Siddarth Mishra-Sharma (NYU) |
2020-02-26 | Astronomical images as a playground to understand OoD behavior of generative models speaker: Vanessa Boehm (Berkeley) |
2020-02-12 | Likelihood Ratios for Out-of-Distribution Detection Paper links are found here: http://www.gatsby.ucl.ac.uk/~balaji/ |
2020-01-29 | Full-Gradient Representation for Neural Network Visualization Speaker: Suraj Srinivas (EPFL) |
2020-01-15 | Lesson from bringing software engineering to machine learning Speakers: Pippin Lee, Cole Clifford (Dessa) |
2019-12-19 | Remote sensing & ML | slides Speaker: Hannah Kerner (UMD) |
2019-12-05 | An Overview of Graph Networks and Physical Simulations Speaker: Jonathan Godwin (DeepMind) |
2019-11-21 | More on deep probabilistic learning Speaker: Francois Lanusse (Berkeley) Bayesian models and active learning Speaker: Mike Walmsley (Oxford) |
2019-11-07 | Deep probabilistic learning Speaker: Francois Lanusse (Berkeley) |
2019-10-24 | Transformers Speaker: David Reiman (UCSC) |
Academic year 2018-2019
2019-06-19 | RE:MARS conference debriefing Speaker: Xavier Prochaska |
2019-05-22 | -- |
2019-05-08 | Speaker: Francois Lanusse (Berkeley) |
2019-04-24 | Pixel Level Morphological Classification using Semantic Segmentation Speaker: Ryan H. Galaxy deblending Speaker: Alexandre Boucaud |
2019-05-10 | Activation Atlases Speaker: Brice Ménard (Johns Hopkins) |
2019-03-13 | Graph neural networks: https://arxiv.org/abs/1812.08434 Speaker: Marc Huertas Company (Paris Observatory) |
2019-02-27 | Deep learning detection of transients Speaker: Dalya Baron (Tel Aviv Univ.) |
2019-02-13 | Style transfer Speaker: Xavier Prochaska (UCSC) |
2019-12-19 | The scattering transform Speaker: Brice Ménard (Johns Hopkins) |
2018-12-05 | Integrated gradients on DLAs Speaker: Xavier Prochaska (UCSC) |
2018-10-24 | DC update -- Speaker: Cheng Latest fun with GANs -- Speaker: Reiman Image classification of DES -- Speaker: Marc |
2018-10-10 | Updates on his U-Net -- Zheng Latest fun with GAN -- Reiman Quantifying classification errors with random forests -- Dalya Baron Identifying informative pixels with neural nets. From saliency to Integrated gradients -- Josh Peek |
2018-09-26 | Summary of Rework Deep Learning Summit Galaxy deblending |
2018-09-12 | Second discussion |
2018-08-29 | First discussion |