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06/06/2023
Residual-based error bound for physics-informed neural networks
Neural networks are universal approximators and are studied for their us...
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12/14/2022
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) are gaining popularity as a met...
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11/29/2022
Improving astroBERT using Semantic Textual Similarity
The NASA Astrophysics Data System (ADS) is an essential tool for researc...
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11/01/2022
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Physics-Informed Neural Networks (PINNs) offer a promising approach to s...
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09/15/2022
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Solutions to differential equations are of significant scientific and en...
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07/12/2022
RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
Reservoir computers (RCs) are among the fastest to train of all neural n...
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07/03/2022
Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems
There have been extensive studies on solving differential equations usin...
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05/13/2022
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
Due to the latest advances in technology, telescopes with significant sk...
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04/04/2022
Con^2DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations
In this work, we present Con^2DA, a simple framework that extends recent...
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12/01/2021
Building astroBERT, a language model for Astronomy Astrophysics
The existing search tools for exploring the NASA Astrophysics Data Syste...
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11/20/2021
Adversarial Sampling for Solving Differential Equations with Neural Networks
Neural network-based methods for solving differential equations have bee...
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11/08/2021
Uncertainty Quantification in Neural Differential Equations
Uncertainty quantification (UQ) helps to make trustworthy predictions ba...
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10/30/2021
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 mo...
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10/21/2021
One-Shot Transfer Learning of Physics-Informed Neural Networks
Solving differential equations efficiently and accurately sits at the he...
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08/25/2021
Unsupervised Reservoir Computing for Solving Ordinary Differential Equations
There is a wave of interest in using unsupervised neural networks for so...
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07/16/2021
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
Accurately learning the temporal behavior of dynamical systems requires ...
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06/07/2021
Encoding Involutory Invariance in Neural Networks
In certain situations, Neural Networks (NN) are trained upon data that o...
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01/15/2021
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function
The activation function plays a fundamental role in the artificial neura...
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10/10/2020
Unsupervised Neural Networks for Quantum Eigenvalue Problems
Eigenvalue problems are critical to several fields of science and engine...
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10/10/2020
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
Studying the dynamics of COVID-19 is of paramount importance to understa...
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08/21/2020
MPCC: Matching Priors and Conditionals for Clustering
Clustering is a fundamental task in unsupervised learning that depends h...
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07/21/2020
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
Solutions to differential equations are of significant scientific and en...
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07/13/2020
Gender Classification and Bias Mitigation in Facial Images
Gender classification algorithms have important applications in many dom...
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03/22/2020
Gravitational Wave Detection and Information Extraction via Neural Networks
Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first...
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02/03/2020
Scalable End-to-end Recurrent Neural Network for Variable star classification
During the last decade, considerable effort has been made to perform aut...
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01/29/2020
Hamiltonian Neural Networks for solving differential equations
There has been a wave of interest in applying machine learning to study ...
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12/04/2019
Streaming Classification of Variable Stars
In the last years, automatic classification of variable stars has receiv...
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11/06/2019
An Information Theory Approach on Deciding Spectroscopic Follow Ups
Classification and characterization of variable phenomena and transient ...
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09/25/2019
Adversarial Variational Domain Adaptation
In this work we address the problem of transferring knowledge obtained f...
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04/08/2019
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
Image quality plays a big role in CNN-based image classification perform...
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03/12/2019
Efficient Optimization of Echo State Networks for Time Series Datasets
Echo State Networks (ESNs) are recurrent neural networks that only train...
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03/08/2019
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves
Within the last years, the classification of variable stars with Machine...
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01/02/2019
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification
Crowdsourcing has become widely used in supervised scenarios where train...
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12/07/2018
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
In real-world applications, it is often expensive and time-consuming to ...
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11/20/2018
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
In this paper we propose a data augmentation method for time series with...
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02/29/2016
Clustering Based Feature Learning on Variable Stars
The success of automatic classification of variable stars strongly depen...
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01/11/2015
Fast and optimal nonparametric sequential design for astronomical observations
The spectral energy distribution (SED) is a relatively easy way for astr...
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04/01/2013
An improved quasar detection method in EROS-2 and MACHO LMC datasets
We present a new classification method for quasar identification in the ...
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03/05/2012
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to impr...
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12/13/2011
