Search Results for author: Mariel Pettee

Found 10 papers, 7 papers with code

AstroCLIP: A Cross-Modal Foundation Model for Galaxies

1 code implementation4 Oct 2023 Liam Parker, Francois Lanusse, Siavash Golkar, Leopoldo Sarra, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Geraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Regaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho

These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification.

Contrastive Learning Morphology classification +4

Learning Likelihood Ratios with Neural Network Classifiers

1 code implementation17 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.

Weakly-Supervised Anomaly Detection in the Milky Way

1 code implementation5 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

Intentional Choreography with Semi-Supervised Recurrent VAEs

no code implementations20 Sep 2022 Mathilde Papillon, Mariel Pettee, Nina Miolane

We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder.

PirouNet: Creating Dance through Artist-Centric Deep Learning

1 code implementation21 Jul 2022 Mathilde Papillon, Mariel Pettee, Nina Miolane

Using Artificial Intelligence (AI) to create dance choreography with intention is still at an early stage.

Symmetry Group Equivariant Architectures for Physics

no code implementations11 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.

BIG-bench Machine Learning

Beyond Imitation: Generative and Variational Choreography via Machine Learning

1 code implementation11 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.

BIG-bench Machine Learning

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