#inverseproblems — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #inverseproblems, aggregated by home.social.
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The second part of our work on dynamic inverse problems is now available:
Jyrki Jauhiainen, Yassine Nabou, & TV: Dynamic inverse problems: Single-loop online algorithms, https://arxiv.org/abs/2606.10951
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New research: “Dynamic inverse problems: Online regularisation theory” with Jyrki Jauhiainen and Yassine Nabou.
https://arxiv.org/abs/2605.26022
This is the first of a two-part study on online methods for dynamic #inverseproblems. The second part, on online #optimization methods, will follow shortly.
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New research: Dang & Valkonen - Leak localisation with a measure source convection–diffusion model
https://arxiv.org/abs/2605.12095
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Optimal estimation (Remote sensing 🛰️)
In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. It is used very commonly in the geosciences, particularly for atmospheric sounding. A matrix inverse problem looks like this: A x → = y → {\displaystyle \mathbf {A} {\vec {x}}={\vec {y}}} The essential concept is to transform the matrix, A, into a c...
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Optimal estimation (Remote sensing 🛰️)
In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. It is used very commonly in the geosciences, particularly for atmospheric sounding. A matrix inverse problem looks like this: A x → = y → {\displaystyle \mathbf {A} {\vec {x}}={\vec {y}}} The essential concept is to transform the matrix, A, into a c...
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Optimal estimation (Remote sensing 🛰️)
In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. It is used very commonly in the geosciences, particularly for atmospheric sounding. A matrix inverse problem looks like this: A x → = y → {\displaystyle \mathbf {A} {\vec {x}}={\vec {y}}} The essential concept is to transform the matrix, A, into a c...
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Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡
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Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡
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Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡
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Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡
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Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡
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🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!
Explore the latest in learning-based methods for inverse problems with top experts.
Register by 15/9 👉 http://indico.desy.de/event/45763/
Don't miss out on this opportunity to expand your skills!
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🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!
Explore the latest in learning-based methods for inverse problems with top experts.
Register by 15/9 👉 http://indico.desy.de/event/45763/
Don't miss out on this opportunity to expand your skills!
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🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!
Explore the latest in learning-based methods for inverse problems with top experts.
Register by 15/9 👉 http://indico.desy.de/event/45763/
Don't miss out on this opportunity to expand your skills!
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🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!
Explore the latest in learning-based methods for inverse problems with top experts.
Register by 15/9 👉 http://indico.desy.de/event/45763/
Don't miss out on this opportunity to expand your skills!
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🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!
Explore the latest in learning-based methods for inverse problems with top experts.
Register by 15/9 👉 http://indico.desy.de/event/45763/
Don't miss out on this opportunity to expand your skills!
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AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/ -
AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/ -
AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/ -
AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/ -
AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/ -
Today @JulianTachella, Matthieu Terris, Dongdon Chen, and Samuel Hurault gave an introduction to Deepinverse library at #DIPOpt workshop.
#inverseproblems #ComputationalImaging #deeplearning -
Today @JulianTachella, Matthieu Terris, Dongdon Chen, and Samuel Hurault gave an introduction to Deepinverse library at #DIPOpt workshop.
#inverseproblems #ComputationalImaging #deeplearning -
Highlights of poster presentation session, second day of #DIPOpt workshop.
1) Continuous Lippmann-Schwinger Intensity Diffraction Tomography, by Olivier Leblanc, @kmlv, and @lowrankjack.
2) Deepinverse Python Library, by Julian Tachella, Dongdong Chen, Samuel Hurault and Matthieu Terris.
#inverseproblems, #computationalimaging -
Highlights of poster presentation session, second day of #DIPOpt workshop.
1) Continuous Lippmann-Schwinger Intensity Diffraction Tomography, by Olivier Leblanc, @kmlv, and @lowrankjack.
2) Deepinverse Python Library, by Julian Tachella, Dongdong Chen, Samuel Hurault and Matthieu Terris.
#inverseproblems, #computationalimaging -
The last talk of the second day of #DIPOpt, by Remi Grinonval, “Rapture of the deep: highs and lows of sparsity in a world of depths”.
#Sparsity #inverseproblems -
The last talk of the second day of #DIPOpt, by Remi Grinonval, “Rapture of the deep: highs and lows of sparsity in a world of depths”.
#Sparsity #inverseproblems -
Next speaker is Mike Davies talking about “Unsupervised Machine Imaging: when is data driven knowledge discovery really possible?”
#DIPOpt #inverseproblems #machinelearning #ComputationalImaging #CompressedSensing -
Next speaker is Mike Davies talking about “Unsupervised Machine Imaging: when is data driven knowledge discovery really possible?”
#DIPOpt #inverseproblems #machinelearning #ComputationalImaging #CompressedSensing -
An excellent talk by Dirk Lorenz, “Learning regularisers - bilevel optimisation or unrolling?” at #DIPOpt workshop, #Lyon.
#inverseproblems #optimisation #regularisation -
An excellent talk by Dirk Lorenz, “Learning regularisers - bilevel optimisation or unrolling?” at #DIPOpt workshop, #Lyon.
#inverseproblems #optimisation #regularisation -
from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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"A Targeted Sampling Strategy for Compressive Cryo FIB Scanning Electron Microscopy"
Joint work with D. Nicholls, J. Wells, A. Robinson, M. Kobylynska, R. Fleck, A. Kirkland, N. Browning
Rosalind Franklin Institute, University of Liverpool, and King's College London
https://arxiv.org/pdf/2211.03494.pdf
#CryoEM #ICASSP2023 #ComputationalImaging #InverseProblems #ElectronMicroscopy -
"A Targeted Sampling Strategy for Compressive Cryo FIB Scanning Electron Microscopy"
Joint work with D. Nicholls, J. Wells, A. Robinson, M. Kobylynska, R. Fleck, A. Kirkland, N. Browning
Rosalind Franklin Institute, University of Liverpool, and King's College London
https://arxiv.org/pdf/2211.03494.pdf
#CryoEM #ICASSP2023 #ComputationalImaging #InverseProblems #ElectronMicroscopy -
Today I attended an excellent seminar by Yunan Yang (ETH Zürich) titled "Optimal transport for learning chaotic dynamics via invariant measures" in the #NumericalAnalysis and #ScientificComputing series in Manchester.
Many interesting ideas and a lot to unpack, so I can't do it justice, but here is a summary.
#OptimalTransport #DynamicalSystems #ParameterIdentification #InverseProblems
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Here's the first one #arxivfeed:
"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks"
https://arxiv.org/abs/2112.08866 -
Here's the first one #arxivfeed:
"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks"
https://arxiv.org/abs/2112.08866 -
Here's the first one #arxivfeed:
"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks"
https://arxiv.org/abs/2112.08866 -
Here's the first one #arxivfeed:
"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks"
https://arxiv.org/abs/2112.08866
