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Found 32 papers, 17 papers with code
Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation
However, in many practical applications this technique is vulnerable to label distribution shifts, sometimes producing catastrophic failure.
SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
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.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
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.
Learnable wavelet neural networks for cosmological inference
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well.
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations.
Can Forward Gradient Match Backpropagation?
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements.
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
These inputs can be selected from a data set or obtained by optimization.
MoMo: Momentum Models for Adaptive Learning Rates
MoMo uses momentum estimates of the batch losses and gradients sampled at each iteration to build a model of the loss function.
Local Learning with Neuron Groups
Local learning is an approach to model-parallelism that removes the standard end-to-end learning setup and utilizes local objective functions to permit parallel learning amongst model components in a deep network.
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
A finer mapping of convolutional neural network layers to the visual cortex
A common approach is to use features extracted from intermediate CNN layers to fit brain encoding models.
Exploring the Optimality of Tight-Frame Scattering Networks
The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet.
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deformation stability.
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
With the use of a replay buffer we show that this approach can be extended to asynchronous settings, where modules can operate and continue to update with possibly large communication delays.
Practical Phase Retrieval: Low-Photon Holography with Untrained Priors
To the best of our knowledge, this is the first work to consider a dataset-free machine learning approach for holographic phase retrieval.
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 magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imaging (CDI).
Decoupled Greedy Learning of CNNs
It is based on a greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification.
Greedy Layerwise Learning Can Scale to ImageNet
Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
Shallow Learning For Deep Networks
Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks.
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals.
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 problems, does not lead to separable factors or a proximal operatorwith a closed-form expression, such as soft thresholding for the $\ell\_1$ penalty.
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
Data-driven HRF estimation for encoding and decoding models
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations.
HRF estimation improves sensitivity of fMRI encoding and decoding models
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal.
