Picture for Joshua Yao-Yu Lin

Joshua Yao-Yu Lin

for the LSST Dark Energy Science Collaboration

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers

Add code
Feb 15, 2023
Viaarxiv icon

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Add code
Oct 09, 2022
Figure 1 for Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Figure 2 for Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Figure 3 for Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Figure 4 for Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Viaarxiv icon

VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks

Add code
Oct 14, 2021
Figure 1 for VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
Figure 2 for VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
Figure 3 for VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
Figure 4 for VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
Viaarxiv icon

AGNet: Weighing Black Holes with Deep Learning

Add code
Aug 17, 2021
Figure 1 for AGNet: Weighing Black Holes with Deep Learning
Figure 2 for AGNet: Weighing Black Holes with Deep Learning
Figure 3 for AGNet: Weighing Black Holes with Deep Learning
Figure 4 for AGNet: Weighing Black Holes with Deep Learning
Viaarxiv icon

Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes

Add code
Jun 18, 2021
Figure 1 for Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Figure 2 for Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Figure 3 for Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Figure 4 for Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Viaarxiv icon

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients

Add code
Mar 22, 2021
Figure 1 for A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
Figure 2 for A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
Figure 3 for A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
Figure 4 for A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
Viaarxiv icon

AGNet: Weighing Black Holes with Machine Learning

Add code
Dec 01, 2020
Figure 1 for AGNet: Weighing Black Holes with Machine Learning
Figure 2 for AGNet: Weighing Black Holes with Machine Learning
Figure 3 for AGNet: Weighing Black Holes with Machine Learning
Figure 4 for AGNet: Weighing Black Holes with Machine Learning
Viaarxiv icon

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant

Add code
Nov 30, 2020
Figure 1 for Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Figure 2 for Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Figure 3 for Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Figure 4 for Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Viaarxiv icon

Learning Principle of Least Action with Reinforcement Learning

Add code
Nov 26, 2020
Figure 1 for Learning Principle of Least Action with Reinforcement Learning
Figure 2 for Learning Principle of Least Action with Reinforcement Learning
Figure 3 for Learning Principle of Least Action with Reinforcement Learning
Figure 4 for Learning Principle of Least Action with Reinforcement Learning
Viaarxiv icon

Anomaly Detection for Multivariate Time Series of Exotic Supernovae

Add code
Oct 21, 2020
Figure 1 for Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Figure 2 for Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Viaarxiv icon