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Found 48 papers, 21 papers with code
Gravitational Duals from Equations of State
Under this map, the equation of state of the field theory is encoded in the black hole solutions of the gravitational theory.
Generating Images of the M87* Black Hole Using GANs
To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model.
One-Shot Transfer Learning for Nonlinear ODEs
We introduce a generalizable approach that combines perturbation method and one-shot transfer learning to solve nonlinear ODEs with a single polynomial term, using Physics-Informed Neural Networks (PINNs).
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) are gaining popularity as a method for solving differential equations.
Improving astroBERT using Semantic Textual Similarity
The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing.
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences.
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Solutions to differential equations are of significant scientific and engineering relevance.
RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
However, RC adoption has lagged other neural network models because of the model's sensitivity to its hyper-parameters (HPs).
Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems
This paper shows that one can mathematically derive explicit error bounds for physics-informed neural networks trained on a class of linear systems of differential equations.
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically.
Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations
We use different loss functions to enforce consistency between the feature representations of associated data pairs of samples.
Physics-Informed Neural Networks for Quantum Eigenvalue Problems
We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems.
Building astroBERT, a language model for Astronomy & Astrophysics
The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e. g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search.
Adversarial Sampling for Solving Differential Equations with Neural Networks
Neural network-based methods for solving differential equations have been gaining traction.
Uncertainty Quantification in Neural Differential Equations
Uncertainty quantification (UQ) helps to make trustworthy predictions based on collected observations and uncertain domain knowledge.
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
The Reynolds-averaged Navier-Stokes (RANS) equations require accurate modeling of the anisotropic Reynolds stress tensor.
One-Shot Transfer Learning of Physics-Informed Neural Networks
In this study, we present a general framework for transfer learning PINNs that results in one-shot inference for linear systems of both ordinary and partial differential equations.
Unsupervised Reservoir Computing for Solving Ordinary Differential Equations
A closed-form formula for the optimal output weights is derived to solve first order linear equations in a backpropagation-free learning process.
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
In this study, we address the challenge of learning from such non-autonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces.
Encoding Involutory Invariances in Neural Networks
In certain situations, neural networks are trained upon data that obey underlying symmetries.
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function
The proposed neuron was tested for problems where the target was a purely global function, or purely local function, or a composition of two global and local functions.
Unsupervised Neural Networks for Quantum Eigenvalue Problems
Eigenvalue problems are critical to several fields of science and engineering.
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves.
MPCC: Matching Priors and Conditionals for Clustering
Our experiments show that adding a learnable prior and augmenting the number of encoder updates improve the quality of the generated samples, obtaining an inception score of 9. 49 $\pm$ 0. 15 and improving the Fr\'echet inception distance over the state of the art by a 46. 9% in CIFAR10.
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
This work develops a novel method for solving differential equations with unsupervised neural networks that applies Generative Adversarial Networks (GANs) to \emph{learn the loss function} for optimizing the neural network.
Gender Classification and Bias Mitigation in Facial Images
We worked to increase classification accuracy and mitigate algorithmic biases on our baseline model trained on the augmented benchmark database.
Solving Differential Equations Using Neural Network Solution Bundles
The time evolution of dynamical systems is frequently described by ordinary differential equations (ODEs), which must be solved for given initial conditions.
Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics
This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses.
Gravitational Wave Detection and Information Extraction via Neural Networks
Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first laboratory to measure the gravitational waves.
Scalable End-to-end Recurrent Neural Network for Variable star classification
Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets.
Hamiltonian neural networks for solving equations of motion
There has been a wave of interest in applying machine learning to study dynamical systems.
Streaming Classification of Variable Stars
Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations.
An Information Theory Approach on Deciding Spectroscopic Follow Ups
We propose a methodology in a probabilistic setting that determines a-priory which objects are worth taking spectrum to obtain better insights, where we focus 'insight' as the type of the object (classification).
Matching Embeddings for Domain Adaptation
In this work we address the problem of transferring knowledge obtained from a vast annotated source domain to a low labeled target domain.
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
To solve this issue, we propose a transfer learning approach optimized to keep into account that in each layer of a CNN some filters are more susceptible to image distortion than others.
Efficient Optimization of Echo State Networks for Time Series Datasets
Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves
Within the Machine Learning literature, dictionary-based methods have been widely used to encode relevant parts of image data.
BIG-bench Machine Learning
Classification Of Variable Stars
+3
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
In this work, we provide a full probabilistic model for a shorter type of queries.
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
Thanks to the combination of a semi-supervised ELBO and parameters sharing across domains, we are able to simultaneously: (i) align all supervised examples of the same class into the same latent Gaussian Mixture component, independently from their domain; (ii) predict the class of unsupervised examples from different domains and use them to better model the occurring shifts.
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
Results show that classifiers trained on T-CGAN-generated data perform the same as classifiers trained on real data, even with very short time series and small training sets.
Robust period estimation using mutual information for multi-band light curves in the synoptic survey era
Robust and efficient methods that can aggregate data from multidimensional sparsely-sampled time series are needed.
Instrumentation and Methods for Astrophysics Information Theory Information Theory
Clustering Based Feature Learning on Variable Stars
Representatives of these patterns, called exemplars, are then used to transform lightcurves of a labeled set into a new representation that can then be used to train an automatic classifier.
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
In this article we present an overview of machine learning and computational intelligence applications to TDA.
Fast and optimal nonparametric sequential design for astronomical observations
The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar objects.
Supervised detection of anomalous light-curves in massive astronomical catalogs
With the aim of taking full advantage of all the information we have about known objects, our method is based on a supervised algorithm.
Automatic Classification of Variable Stars in Catalogs with missing data
We present an automatic classification method for astronomical catalogs with missing data.
An improved quasar detection method in EROS-2 and MACHO LMC datasets
We present a new classification method for quasar identification in the EROS-2 and MACHO datasets based on a boosted version of Random Forest classifier.
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
The paper applies this framework for data where each task is a phase-shifted periodic time series.
