
OneShot Transfer Learning of PhysicsInformed Neural Networks
Solving differential equations efficiently and accurately sits at the he...
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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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PortHamiltonian Neural Networks for Learning Explicit TimeDependent Dynamical Systems
Accurately learning the temporal behavior of dynamical systems requires ...
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Encoding Involutory Invariance in Neural Networks
In certain situations, Neural Networks (NN) are trained upon data that o...
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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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Unsupervised Neural Networks for Quantum Eigenvalue Problems
Eigenvalue problems are critical to several fields of science and engine...
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Semisupervised Neural Networks solve an inverse problem for modeling Covid19 spread
Studying the dynamics of COVID19 is of paramount importance to understa...
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MPCC: Matching Priors and Conditionals for Clustering
Clustering is a fundamental task in unsupervised learning that depends h...
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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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Gender Classification and Bias Mitigation in Facial Images
Gender classification algorithms have important applications in many dom...
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Gravitational Wave Detection and Information Extraction via Neural Networks
Laser Interferometer GravitationalWave Observatory (LIGO) was the first...
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Scalable Endtoend Recurrent Neural Network for Variable star classification
During the last decade, considerable effort has been made to perform aut...
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Hamiltonian Neural Networks for solving differential equations
There has been a wave of interest in applying machine learning to study ...
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Streaming Classification of Variable Stars
In the last years, automatic classification of variable stars has receiv...
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An Information Theory Approach on Deciding Spectroscopic Follow Ups
Classification and characterization of variable phenomena and transient ...
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Adversarial Variational Domain Adaptation
In this work we address the problem of transferring knowledge obtained f...
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Improving Image Classification Robustness through Selective CNNFilters FineTuning
Image quality plays a big role in CNNbased image classification perform...
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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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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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A Full Probabilistic Model for Yes/No Type Crowdsourcing in MultiClass Classification
Crowdsourcing has become widely used in supervised scenarios where train...
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Deep Variational Transfer: Transfer Learning through Semisupervised Deep Generative Models
In realworld applications, it is often expensive and timeconsuming to ...
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TCGAN: 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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Clustering Based Feature Learning on Variable Stars
The success of automatic classification of variable stars strongly depen...
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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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An improved quasar detection method in EROS2 and MACHO LMC datasets
We present a new classification method for quasar identification in the ...
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Infinite Shiftinvariant Grouped Multitask Learning for Gaussian Processes
Multitask learning leverages shared information among data sets to impr...
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Period Estimation in Astronomical Time Series Using Slotted Correntropy
In this letter, we propose a method for period estimation in light curve...
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