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07/24/2023
Learnable wavelet neural networks for cosmological inference
Convolutional neural networks (CNNs) have been shown to both extract mor...
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06/26/2023
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
Separating signals from an additive mixture may be an unnecessarily hard...
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06/12/2023
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
The internal functional behavior of trained Deep Neural Networks is noto...
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06/12/2023
Can Forward Gradient Match Backpropagation?
Forward Gradients - the idea of using directional derivatives in forward...
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01/18/2023
Local Learning with Neuron Groups
Traditional deep network training methods optimize a monolithic objectiv...
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07/18/2022
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a sta...
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04/15/2022
Wavelet Moments for Cosmological Parameter Estimation
Extracting non-Gaussian information from the non-linear regime of struct...
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01/04/2022
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS...
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09/22/2021
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulati...
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07/20/2021
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deform...
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06/11/2021
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
A commonly cited inefficiency of neural network training using back-prop...
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12/14/2020
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Phase retrieval is the inverse problem of recovering a signal from magni...
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01/23/2019
Decoupled Greedy Learning of CNNs
A commonly cited inefficiency of neural network training by back-propaga...
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12/29/2018
Greedy Layerwise Learning Can Scale to ImageNet
Shallow supervised 1-hidden layer neural networks have a number of favor...
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12/28/2018
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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05/01/2018
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule p...
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12/22/2015
FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging
The total variation (TV) penalty, as many other analysis-sparsity proble...
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12/12/2014
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimag...
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08/10/2013
