Search Results for author:
Found 9 papers, 6 papers with code
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models
We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation models that bridge the gap between diverse observational modalities.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
xVal: A Continuous Number Encoding for Large Language Models
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.
Learning Likelihood Ratios with Neural Network Classifiers
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
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
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder.
PirouNet: Creating Dance through Artist-Centric Deep Learning
Using Artificial Intelligence (AI) to create dance choreography with intention is still at an early stage.
Symmetry Group Equivariant Architectures for Physics
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.
Beyond Imitation: Generative and Variational Choreography via Machine Learning
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.
