1 code implementation • 4 Oct 2023 • Francois Lanusse, Liam Parker, Siavash Golkar, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Geraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Regaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation models that bridge the gap between diverse observational modalities.
1 code implementation • 4 Oct 2023 • Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
2 code implementations • 4 Oct 2023 • Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.
no code implementations • 17 May 2023 • Shahzar Rizvi, Mariel Pettee, Benjamin Nachman
The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more.
1 code implementation • 5 May 2023 • Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches.
Supervised Anomaly Detection Weakly-supervised Anomaly Detection
no code implementations • 20 Sep 2022 • Mathilde Papillon, Mariel Pettee, Nina Miolane
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder.
1 code implementation • 21 Jul 2022 • Mathilde Papillon, Mariel Pettee, Nina Miolane
Using Artificial Intelligence (AI) to create dance choreography with intention is still at an early stage.
no code implementations • 11 Mar 2022 • Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.
1 code implementation • 11 Jul 2019 • Mariel Pettee, Chase Shimmin, Douglas Duhaime, Ilya Vidrin
Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences of choreography as well as tunable variations on input choreographic sequences.