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Found 14 papers, 10 papers with code
Self-supervised Representation Learning From Random Data Projectors
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series.
Self-supervised similarity search for large scientific datasets
We present the use of self-supervised learning to explore and exploit large unlabeled datasets.
Mining for Strong Gravitational Lenses with Self-supervised Learning
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9.
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
Statistical exploration of halo anisotropic clustering and intrinsic alignments with the mass-Peak Patch algorithm
The anisotropy or triaxiality of massive dark matter haloes largely defines the structure of the cosmic web, in particular the filaments that join the haloes together.
Cosmology and Nongalactic Astrophysics
Self-Supervised Representation Learning for Astronomical Images
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data.
Nonlinear 3D Cosmic Web Simulation with Heavy-Tailed Generative Adversarial Networks
Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large.
Cosmology and Nongalactic Astrophysics Computational Physics
Deconfusing intensity maps with neural networks
We evaluate the trained CNN on a number of noise scenarios in order to determine how robust the network predictions are for application to realistic data.
Cosmology and Nongalactic Astrophysics
The mass-Peak Patch algorithm for fast generation of deep all-sky dark matter halo catalogues and its N-Body validation
We present a detailed description and validation of our massively-parallel update to the mass-Peak Patch method, a fully predictive initial-space algorithm to quickly generate dark matter halo catalogues in very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
The Simons Observatory: Science goals and forecasts
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues
For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations.
