Atılım Güneş Baydin : Publications
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[1]
A semantic network−based evolutionary algorithm for computational creativity
Atılım Güneş Baydin‚ Ramon López de Mántaras and Santiago Ontañón
In Evolutionary Intelligence. Vol. 8. No. 1. Pages 3–21. 2015.
Details about A semantic network−based evolutionary algorithm for computational creativity | BibTeX data for A semantic network−based evolutionary algorithm for computational creativity | DOI (10.1007/s12065-014-0119-1)
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[2]
Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer
Michael D. Himes‚ Joseph Harrington‚ Adam D. Cobb‚ Atılım Güneş Baydin‚ Frank Soboczenski‚ Molly D. O'Beirne‚ Simone Zorzan‚ David C. Wright‚ Zacchaeus Scheffer‚ Shawn D. Domagal−Goldman and Giada N. Arney
In The Planetary Science Journal. Vol. 3. No. 4. Pages 236–250. 2022.
Details about Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer | BibTeX data for Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer | DOI (10.3847/PSJ/abe3fd) | Link to Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer
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[3]
Alpha MAML: Adaptive Model−Agnostic Meta−Learning
Harkirat Behl‚ Atılım Güneş Baydin and Philip H.S. Torr
In 6th ICML Workshop on Automated Machine Learning‚ Thirty−sixth International Conference on Machine Learning (ICML 2019)‚ Long Beach‚ CA‚ US. 2019.
Details about Alpha MAML: Adaptive Model−Agnostic Meta−Learning | BibTeX data for Alpha MAML: Adaptive Model−Agnostic Meta−Learning
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[4]
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi‚ Adam Ścibior‚ Andreas Munk‚ Mehrdad Ghadiri‚ Atılım Güneş Baydin‚ Bradley Gram−Hansen‚ Christian Schroeder de Witt‚ Robert Zinkov‚ Philip H.S. Torr‚ Tom Rainforth‚ Yee Whye Teh and Frank Wood
In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS). 2022.
Details about Amortized Rejection Sampling in Universal Probabilistic Programming | BibTeX data for Amortized Rejection Sampling in Universal Probabilistic Programming
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[5]
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi‚ Adam Ścibior‚ Andreas Munk‚ Mehrdad Ghadiri‚ Atılım Güneş Baydin‚ Bradley Gram−Hansen‚ Christian Schroeder de Witt‚ Robert Zinkov‚ Philip H.S. Torr‚ Tom Rainforth‚ Yee Whye Teh and Frank Wood
In International Conference on Probabilistic Programming (PROBPROG 2020)‚ Cambridge‚ MA‚ United States. 2020.
Details about Amortized Rejection Sampling in Universal Probabilistic Programming | BibTeX data for Amortized Rejection Sampling in Universal Probabilistic Programming | Link to Amortized Rejection Sampling in Universal Probabilistic Programming
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[6]
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Adam D. Cobb‚ Michael D. Himes‚ Frank Soboczenski‚ Simone Zorzan‚ Molly D. O’Beirne‚ Atılım Güneş Baydin‚ Yarin Gal‚ Shawn D. Domagal−Goldman‚ Giada N. Arney and Daniel Angerhausen
In The Astronomical Journal. Vol. 158. No. 1. 2019.
Details about An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval | BibTeX data for An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval | DOI (10.3847/1538-3881/ab2390) | Link to An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
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[7]
Attention for Inference Compilation
William Harvey‚ Andreas Munk‚ Alexander Bergholm‚ Atılım Güneş Baydin and Frank Wood
In 12th International Conference on Simulation and Modeling Methodologies‚ Technologies and Applications (SIMULTECH). 2022.
Details about Attention for Inference Compilation | BibTeX data for Attention for Inference Compilation
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[8]
Attention for Inference Compilation
William Harvey‚ Andreas Munk‚ Atılım Güneş Baydin‚ Alexander Bergholm and Frank Wood
In International Conference on Probabilistic Programming (PROBPROG 2020)‚ Cambridge‚ MA‚ United States. 2020.
Details about Attention for Inference Compilation | BibTeX data for Attention for Inference Compilation | Link to Attention for Inference Compilation
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[9]
Auto−Calibration of Remote Sensing Solar Telescopes with Deep Learning
Brad Neuberg‚ Souvik Bose‚ Valentina Salvatelli‚ Luiz F. Guedes dos Santos‚ Mark Cheung‚ Miho Janvier‚ Atılım Güneş Baydin‚ Yarin Gal and Meng Jin
In Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Auto−Calibration of Remote Sensing Solar Telescopes with Deep Learning | BibTeX data for Auto−Calibration of Remote Sensing Solar Telescopes with Deep Learning
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[10]
Auto−calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning
Mark Cheung‚ Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Brad Neuberg‚ Valentina Salvatelli‚ Atılım Güneş Baydin‚ Miho Janvier and Meng Jin
In American Geophysical Union (AGU) Fall Meeting‚ San Francisco‚ CA‚ United States‚ December 9–13‚ 2019. 2019.
Details about Auto−calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning | BibTeX data for Auto−calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning | Link to Auto−calibration and reconstruction of SDO’s Atmospheric Imaging Assembly channels with Deep Learning
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[11]
AutoSimulate: (Quickly) Learning Synthetic Data Generation
Harkirat Singh Behl‚ Atılım Güneş Baydin‚ Ran Gal‚ Philip H. S. Torr and Vibhav Vineet
In 16th European Conference on Computer Vision (ECCV). 2020.
Details about AutoSimulate: (Quickly) Learning Synthetic Data Generation | BibTeX data for AutoSimulate: (Quickly) Learning Synthetic Data Generation
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[12]
Automated generation of cross−domain analogies via evolutionary computation
Atılım Güneş Baydin‚ Ramon López de Mántaras and Santiago Ontañón
In Proceedings of the International Conference on Computational Creativity (ICCC 2012)‚ Dublin‚ Ireland‚ May 30–June 1‚ 2012. Pages 25–32. 2012.
Details about Automated generation of cross−domain analogies via evolutionary computation | BibTeX data for Automated generation of cross−domain analogies via evolutionary computation
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[13]
Automatic differentiation in machine learning: a survey
Atılım Güneş Baydin‚ Barak A. Pearlmutter‚ Alexey Andreyevich Radul and Jeffrey Mark Siskind
In Journal of Machine Learning Research (JMLR). Vol. 18. No. 153. Pages 1−43. 2018.
Details about Automatic differentiation in machine learning: a survey | BibTeX data for Automatic differentiation in machine learning: a survey | Link to Automatic differentiation in machine learning: a survey
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[14]
Automatic differentiation of algorithms for machine learning
Atılım Güneş Baydin and Barak A. Pearlmutter
In AutoML Workshop‚ International Conference on Machine Learning (ICML)‚ Beijing‚ China‚ June 21–26‚ 2014. 2014.
Details about Automatic differentiation of algorithms for machine learning | BibTeX data for Automatic differentiation of algorithms for machine learning
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[15]
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Frank Soboczenski‚ Michael D. Himes‚ Molly D. O'Beirne‚ Simone Zorzan‚ Atılım Güneş Baydin‚ Adam D. Cobb‚ Yarin Gal‚ Daniel Angerhausen‚ Massimo Mascaro‚ Giada N. Arney and Shawn D. Domagal−Goldman
In Third workshop on Bayesian Deep Learning (NeurIPS 2018)‚ Montreal‚ Canada. 2018.
Details about Bayesian Deep Learning for Exoplanet Atmospheric Retrieval | BibTeX data for Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
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[16]
Black−Box Optimization with Local Generative Surrogates
Vladislav Belavin‚ Sergey Shirobokov‚ Michael Aaron Kagan‚ Andrey Ustyuzhanin and Atılım Güneş Baydin
In 4th IML Machine Learning Workshop‚ 19–22 October 2020‚ Inter−experimental Machine Learning (IML) Working Group‚ CERN. 2020.
Details about Black−Box Optimization with Local Generative Surrogates | BibTeX data for Black−Box Optimization with Local Generative Surrogates | Link to Black−Box Optimization with Local Generative Surrogates
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[17]
Black−Box Optimization with Local Generative Surrogates
Sergey Shirobokov‚ Vladislav Belavin‚ Michael Kagan‚ Andrey Ustyuzhanin and Atılım Güneş Baydin
In Advances in Neural Information Processing Systems 34 (NeurIPS). 2020.
Details about Black−Box Optimization with Local Generative Surrogates | BibTeX data for Black−Box Optimization with Local Generative Surrogates
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[18]
CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation
Atılım Güneş Baydin‚ Ramon López de Mántaras‚ Simeon Simoff and Carles Sierra
In Ashwin Ram and Nirmalie Wiratunga, editors, Case−Based Reasoning Research and Development. Vol. 6880 of Lecture Notes in Computer Science. Pages 378–392. Springer Berlin Heidelberg. 2011.
Details about CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation | BibTeX data for CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation | DOI (10.1007/978-3-642-23291-6_28) | Link to CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation
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[19]
Cloud Computing at NASA's Frontier Development Lab
Mark Cheung‚ Andrés Munoz−Jaramillo‚ Paul Wright‚ Asti Bhatt‚ Ignacio López−Francos‚ Atılım Güneş Baydin‚ Piotr Bilinski‚ Daniel Angerhausen and Miho Janvier
In Next Generation Cloud Research Infrastructure‚ Princeton‚ NJ‚ United States‚ November 11–12‚ 2019. 2019.
Details about Cloud Computing at NASA's Frontier Development Lab | BibTeX data for Cloud Computing at NASA's Frontier Development Lab | Link to Cloud Computing at NASA's Frontier Development Lab
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[20]
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
Kara Lamb‚ Garima Malhotra‚ Athanasios Vlontzos‚ Edward Wagstaff‚ Atılım Güneş Baydin‚ Anahita Bhiwandiwalla‚ Yarin Gal‚ Alfredo Kalaitzis‚ Anthony Reina and Asti Bhatt
In Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder | BibTeX data for Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
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[21]
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation
Andreas Munk‚ Adam Ścibior‚ Atılım Güneş Baydin‚ Andrew Stewart‚ Goran Fernlund‚ Anoush Poursartip and Frank Wood
In International Conference on Probabilistic Programming (PROBPROG 2020)‚ Cambridge‚ MA‚ United States. 2020.
Details about Deep Probabilistic Surrogate Networks for Universal Simulator Approximation | BibTeX data for Deep Probabilistic Surrogate Networks for Universal Simulator Approximation | Link to Deep Probabilistic Surrogate Networks for Universal Simulator Approximation
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[22]
Detecting and Quantifying Malicious Activity with Simulation−based Inference
Andrew Gambardella‚ Bogdan State‚ Naeemullah Khan‚ Kleovoulos Tsourides‚ Philip Torr and Atılım Güneş Baydin
In ICML Workshop on Socially Responsible Machine Learning. 2021.
Details about Detecting and Quantifying Malicious Activity with Simulation−based Inference | BibTeX data for Detecting and Quantifying Malicious Activity with Simulation−based Inference
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[23]
DiffSharp: An AD Library for .NET Languages
Atılım Güneş Baydin‚ Barak A. Pearlmutter and Jeffrey Mark Siskind
In 7th International Conference on Algorithmic Differentiation‚ Christ Church Oxford‚ UK‚ September 12–15‚ 2016. 2016.
Details about DiffSharp: An AD Library for .NET Languages | BibTeX data for DiffSharp: An AD Library for .NET Languages
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[24]
DiffSharp: Automatic Differentiation Library
Atılım Güneş Baydin and Barak A. Pearlmutter
In International Conference on Machine Learning (ICML) Workshop on Machine Learning Open Source Software 2015: Open Ecosystems‚ Lille‚ France‚ July 10‚ 2015. 2015.
Details about DiffSharp: Automatic Differentiation Library | BibTeX data for DiffSharp: Automatic Differentiation Library
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[25]
Differentiable Programming in High−Energy Physics
Atılım Güneş Baydin‚ Kyle Cranmer‚ Matthew Feickert‚ Lindsey Gray‚ Lukas Heinrich‚ Alexander Held‚ Andrew Melo‚ Mark Neubauer‚ Jannicke Pearkes‚ Nathan Simpson‚ Nick Smith‚ Giordon Stark‚ Savannah Thais‚ Vassil Vassilev and Gordon Watts
In Snowmass 2021 Letters of Interest (LOI)‚ Division of Particles and Fields (DPF)‚ American Physical Society. 2020.
Details about Differentiable Programming in High−Energy Physics | BibTeX data for Differentiable Programming in High−Energy Physics | Link to Differentiable Programming in High−Energy Physics
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[26]
Differentiating the Black−Box: Optimization with Local Generative Surrogates
Sergey Shirobokov‚ Vladislav Belavin‚ Michael Kagan‚ Andrey Ustyuzhanin and Atılım Güneş Baydin
In Applied Machine Learning Days (AMLD) EPFL‚ Lausanne‚ Switzerland‚ January 25–29‚ 2020. 2020.
Details about Differentiating the Black−Box: Optimization with Local Generative Surrogates | BibTeX data for Differentiating the Black−Box: Optimization with Local Generative Surrogates
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[27]
Dissipative Particle Dynamics and Coarse−Graining: Review of Existing Techniques‚ Trials with Evolutionary Computation
Atılım Güneş Baydin
Master's Thesis Department of Applied Physics‚ Chalmers University of Technology. Göteborg‚ Sweden. 2008.
Details about Dissipative Particle Dynamics and Coarse−Graining: Review of Existing Techniques‚ Trials with Evolutionary Computation | BibTeX data for Dissipative Particle Dynamics and Coarse−Graining: Review of Existing Techniques‚ Trials with Evolutionary Computation
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[28]
Domain Invariant Representation Learning with Domain Density Transformations
Tuan Nguyen‚ Toan Tran‚ Yarin Gal and Atılım Güneş Baydin
In Advances in Neural Information Processing Systems 35 (NeurIPS). 2021.
Details about Domain Invariant Representation Learning with Domain Density Transformations | BibTeX data for Domain Invariant Representation Learning with Domain Density Transformations
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[29]
Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation
Stefano Bonasera‚ Giacomo Acciarini‚ Jorge A. Pérez−Hernández‚ Bernard Benson‚ Edward Brown‚ Eric Sutton‚ Moriba K. Jah‚ Christopher Bridges and Atılım Güneş Baydin
In Bayesian Deep Learning workshop‚ NeurIPS 2021. 2021.
Details about Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation | BibTeX data for Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation
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[30]
Efficient Bayesian Inference for Nested Simulators
Bradley Gram−Hansen‚ Christian Schroeder de Witt‚ Robert Zinkov‚ Saeid Naderiparizi‚ Adam Scibior‚ Andreas Munk‚ Frank Wood‚ Mehrdad Ghadiri‚ Philip Torr‚ Yee Whye Teh‚ Atılım Güneş Baydin and Tom Rainforth
In Second Symposium on Advances in Approximate Bayesian Inference (AABI)‚ Vancouver‚ Canada‚ 8 December 2019. 2019.
Details about Efficient Bayesian Inference for Nested Simulators | BibTeX data for Efficient Bayesian Inference for Nested Simulators
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[31]
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin‚ Lukas Heinrich‚ Wahid Bhimji‚ Lei Shao‚ Saeid Naderiparizi‚ Andreas Munk‚ Jialin Liu‚ Bradley Gram−Hansen‚ Gilles Louppe‚ Lawrence Meadows‚ Philip Torr‚ Victor Lee‚ Prabhat‚ Kyle Cranmer and Frank Wood
In Advances in Neural Information Processing Systems 33 (NeurIPS). 2019.
Details about Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model | BibTeX data for Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
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[32]
End−to−end Training of Differentiable Pipelines Across Machine Learning Frameworks
Mitar Milutinovic‚ Atılım Güneş Baydin‚ Robert Zinkov‚ William Harvey‚ Dawn Song‚ Frank Wood and Wade Shen
In Neural Information Processing Systems (NIPS) 2017 Autodiff Workshop: The Future of Gradient−based Machine Learning Software and Techniques‚ Long Beach‚ CA‚ US‚ December 9‚ 2017. 2017.
Details about End−to−end Training of Differentiable Pipelines Across Machine Learning Frameworks | BibTeX data for End−to−end Training of Differentiable Pipelines Across Machine Learning Frameworks
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[33]
Estimating the Impact of Coordinated Inauthentic Behavior on Content Recommendations in Social Networks
Swapneel Mehta‚ Bogdan State‚ Richard Bonneau‚ Jonathan Nagler‚ Philip Torr and Atılım Güneş Baydin
In AI for Agent−Based Modelling Workshop (AI4ABM) at the International Conference on Machine Learning (ICML) 2022. 2022.
Details about Estimating the Impact of Coordinated Inauthentic Behavior on Content Recommendations in Social Networks | BibTeX data for Estimating the Impact of Coordinated Inauthentic Behavior on Content Recommendations in Social Networks
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[34]
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin‚ Lei Shao‚ Wahid Bhimji‚ Lukas Heinrich‚ Lawrence F. Meadows‚ Jialin Liu‚ Andreas Munk‚ Saeid Naderiparizi‚ Bradley Gram−Hansen‚ Gilles Louppe‚ Mingfei Ma‚ Xiaohui Zhao‚ Philip Torr‚ Victor Lee‚ Kyle Cranmer‚ Prabhat and Frank Wood
In Proceedings of the International Conference for High Performance Computing‚ Networking‚ Storage and Analysis. New York‚ NY‚ USA. 2019. Association for Computing Machinery.
Details about Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale | BibTeX data for Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale | DOI (10.1145/3295500.3356180) | Link to Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
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[35]
Evolution of central pattern generators for the control of a five−link bipedal walking mechanism
Atılım Güneş Baydin
In Paladyn‚ Journal of Behavioral Robotics. Vol. 3. No. 1. Pages 45–53. 2012.
Details about Evolution of central pattern generators for the control of a five−link bipedal walking mechanism | BibTeX data for Evolution of central pattern generators for the control of a five−link bipedal walking mechanism | DOI (10.2478/s13230-012-0019-y)
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[36]
Evolution of ideas: A novel memetic algorithm based on semantic networks
Atılım Güneş Baydin and Ramon López de Mántaras
In Proceedings of the IEEE Congress on Evolutionary Computation‚ CEC 2012‚ IEEE World Congress On Computational Intelligence‚ WCCI 2012‚ Brisbane‚ Australia‚ June 10–15‚ 2012. Pages 1–8. 2012.
Details about Evolution of ideas: A novel memetic algorithm based on semantic networks | BibTeX data for Evolution of ideas: A novel memetic algorithm based on semantic networks | DOI (10.1109/CEC.2012.6252886)
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[37]
Evolutionary Adaptation in Case−Based Reasoning: An Application to Inter−Domain Analogies for Mediation
Atılım Güneş Baydin
PhD Thesis Universitat Autònoma de Barcelona. Barcelona‚ Spain. 2013.
Details about Evolutionary Adaptation in Case−Based Reasoning: An Application to Inter−Domain Analogies for Mediation | BibTeX data for Evolutionary Adaptation in Case−Based Reasoning: An Application to Inter−Domain Analogies for Mediation | DOI (doi:10803/129294)
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[38]
Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning
M. Himes‚ A. Cobb‚ A. Baydin‚ F. Soboczenski‚ S. Zorzan‚ M. O'Beirne‚ G.N. Arney‚ S. Domagal−Goldman‚ D. Angerhausen and Y. Gal
In American Astronomical Society Meeting on Extreme Solar Systems IV‚ Reykjavik‚ Iceland‚ August 19–23‚ 2019. 2019.
Details about Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning | BibTeX data for Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning | Link to Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning
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[39]
Expanding the capabilities of NASA's Solar Dynamics Observatory
Souvik Bose‚ Brad Neuberg‚ Valentina Salvatelli‚ Luiz F. Guedes dos Santos‚ Mark Cheung‚ Miho Janvier‚ Atılım Güneş Baydin and Meng Jin
NASA Frontier Development Lab Technical Memorandum. 2019.
Details about Expanding the capabilities of NASA's Solar Dynamics Observatory | BibTeX data for Expanding the capabilities of NASA's Solar Dynamics Observatory
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[40]
Expanding the capabilities of NASA's Solar Dynamics Observatory
Souvik Bose‚ Brad Neuberg‚ Valentina Salvatelli‚ Luiz F. G. dos Santos‚ Mark Cheung‚ Miho Janvier‚ Atılım Güneş Baydin and Meng Jin
NASA Frontier Development Lab Technical Memorandum. 2019.
Details about Expanding the capabilities of NASA's Solar Dynamics Observatory | BibTeX data for Expanding the capabilities of NASA's Solar Dynamics Observatory
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[41]
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
Valentina Salvatelli‚ Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Brad Neuberg‚ Mark Cheung‚ Miho Janvier‚ Meng Jin‚ Yarin Gal and Atılım Güneş Baydin
In The Astrophysical Journal. Vol. 937. No. 2. 2022.
Details about Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation | BibTeX data for Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation | DOI (10.3847/1538-4357/ac867b) | Link to Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
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[42]
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
Valentina Salvatelli‚ Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Brad Neuberg‚ Mark Cheung‚ Miho Janvier‚ Meng Jin‚ Yarin Gal and Atılım Güneş Baydin
In The Astrophysical Journal. 2022 (to appear).
Details about Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation | BibTeX data for Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
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[43]
Flood Detection On Low Cost Orbital Hardware
Gonzalo Mateo−Garcia‚ Silviu Oprea‚ Lewis Smith‚ Joshua Veitch−Michaelis‚ Atılım Güneş Baydin and Dietmar Backes
ESA Frontier Development Lab Technical Memorandum. 2019.
Details about Flood Detection On Low Cost Orbital Hardware | BibTeX data for Flood Detection On Low Cost Orbital Hardware
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[44]
Flood Detection On Low Cost Orbital Hardware
Gonzalo Mateo−Garcia‚ Silviu Oprea‚ Lewis Smith‚ Joshua Veitch−Michaelis‚ Atılım Güneş Baydin and Dietmar Backes
In Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop‚ 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Flood Detection On Low Cost Orbital Hardware | BibTeX data for Flood Detection On Low Cost Orbital Hardware
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[45]
From Biohints to Confirmed Evidence of Life: Possible Metabolisms Within Extraterrestrial Environmental Substrates
Michael D. Himes‚ Molly D. O’Beirne‚ Frank Soboczenski‚ Simone Zorzan‚ Atılım Güneş Baydin‚ Adam Cobb‚ Daniel Angerhausen‚ Giada N. Arney and Shawn D. Domagal−Goldman
NASA Frontier Development Lab Technical Memorandum. 2018.
Details about From Biohints to Confirmed Evidence of Life: Possible Metabolisms Within Extraterrestrial Environmental Substrates | BibTeX data for From Biohints to Confirmed Evidence of Life: Possible Metabolisms Within Extraterrestrial Environmental Substrates
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[46]
Heliophysics – Solar Drag: Learning how the Sun affects spacecraft orbits
Bernard Benson‚ Stefano Bonasera‚ Edward Brown‚ Jorge A. Pérez−Hernández‚ Moriba K. Jah‚ Eric Sutton‚ Giacomo Acciarini‚ Christopher P. Bridges and Atılım Güneş Baydin
NASA Frontier Development Lab Technical Memorandum. 2021.
Details about Heliophysics – Solar Drag: Learning how the Sun affects spacecraft orbits | BibTeX data for Heliophysics – Solar Drag: Learning how the Sun affects spacecraft orbits
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[47]
Hijacking Malaria Simulators with Probabilistic Programming
Bradley Gram−Hansen‚ Christian Schroeder‚ Philip H.S. Torr‚ Yee Whye Teh‚ Tom Rainforth and Atılım Güneş Baydin
In ICML Workshop on AI for Social Good‚ Thirty−sixth International Conference on Machine Learning (ICML 2019)‚ Long Beach‚ CA‚ US. 2019.
Details about Hijacking Malaria Simulators with Probabilistic Programming | BibTeX data for Hijacking Malaria Simulators with Probabilistic Programming
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[48]
INARA: A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra
Molly D. O’Beirne‚ Michael D. Himes‚ Frank Soboczenski‚ Simone Zorzan‚ Adam Cobb‚ Atılım Güneş Baydin‚ Yarin Gal‚ Daniel Angerhausen‚ Massimo Mascaro‚ Giada N. Arney and Shawn D. Domagal−Goldman
In Astrobiology Science Conference (AbSciCon 2019)‚ Bellevue‚ Washington‚ June 24–28‚ 2019. 2019.
Details about INARA: A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra | BibTeX data for INARA: A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra | Link to INARA: A Machine Learning Retrieval Framework with a Data Set of 3 Million Simulated Exoplanet Atmospheric Spectra
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[49]
Improvements to Inference Compilation for Probabilistic Programming in Large−Scale Scientific Simulators
Mario Lezcano Casado‚ Atılım Güneş Baydin‚ David Martinez Rubio‚ Tuan Anh Le‚ Frank Wood‚ Lukas Heinrich‚ Gilles Louppe‚ Kyle Cranmer‚ Wahid Bhimji‚ Karen Ng and Prabhat
In Neural Information Processing Systems (NIPS) 2017 workshop on Deep Learning for Physical Sciences (DLPS)‚ Long Beach‚ CA‚ US‚ December 8‚ 2017. 2017.
Details about Improvements to Inference Compilation for Probabilistic Programming in Large−Scale Scientific Simulators | BibTeX data for Improvements to Inference Compilation for Probabilistic Programming in Large−Scale Scientific Simulators
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[50]
Inference Compilation and Universal Probabilistic Programming
Tuan Anh Le‚ Atılım Güneş Baydin and Frank Wood
In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 54 of Proceedings of Machine Learning Research. Pages 1338–1348. Fort Lauderdale‚ FL‚ USA. 2017. PMLR.
Details about Inference Compilation and Universal Probabilistic Programming | BibTeX data for Inference Compilation and Universal Probabilistic Programming
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[51]
Inferring molecular complexity from mass spectrometry data using machine learning
Timothy D. Gebhard‚ Aaron Bell‚ Jian Gong‚ Jaden J.A. Hastings‚ George M. Fricke‚ Nathalie Cabrol‚ Scott Sandford‚ Michael Phillips‚ Kimberley Warren−Rhodes and Atılım Güneş Baydin
In Machine Learning and the Physical Sciences workshop‚ NeurIPS 2022. 2022.
Details about Inferring molecular complexity from mass spectrometry data using machine learning | BibTeX data for Inferring molecular complexity from mass spectrometry data using machine learning
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[52]
Karman – a Machine Learning Software Package for Benchmarking Thermospheric Density Models
Giacomo Acciarini‚ Edward Brown‚ Christopher Bridges‚ Atılım Güneş Baydin‚ Thomas E. Berger and Madhulika Guhathakurta
In Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference‚ 19–22 September 2023. 2023.
Details about Karman – a Machine Learning Software Package for Benchmarking Thermospheric Density Models | BibTeX data for Karman – a Machine Learning Software Package for Benchmarking Thermospheric Density Models
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[53]
Kessler: a Machine Learning Library for Space Collision Avoidance
Giacomo Acciarini‚ Francesco Pinto‚ Sascha Metz‚ Sarah Boufelja‚ Sylvester Kaczmarek‚ Klaus Merz‚ José A. Martinez−Heras‚ Francesca Letizia‚ Christopher Bridges and Atılım Güneş Baydin
In 8th European Conference on Space Debris. 2021.
Details about Kessler: a Machine Learning Library for Space Collision Avoidance | BibTeX data for Kessler: a Machine Learning Library for Space Collision Avoidance
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[54]
Learning the solar latent space: sigma−variational autoencoders for multiple channel solar imaging
Edward Brown‚ Stefano Bonasera‚ Bernard Benson‚ Jorge A. Pérez−Hernández‚ Giacomo Acciarini‚ Atılım Güneş Baydin‚ Christopher Bridges‚ Meng Jin‚ Eric Sutton and Moriba K. Jah
In Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021). 2021.
Details about Learning the solar latent space: sigma−variational autoencoders for multiple channel solar imaging | BibTeX data for Learning the solar latent space: sigma−variational autoencoders for multiple channel solar imaging
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[55]
Living With Our Star: Enhanced Predictability of GNSS Disturbances
Kara Lamb‚ Garima Malhotra‚ Athanasios Vlontzos‚ Edward Wagstaff‚ Asti Bhatt‚ Atılım Güneş Baydin‚ Anahita Bhiwandiwalla‚ Yarin Gal‚ Alfredo Kalaitzis and Tony Reina
NASA Frontier Development Lab Technical Memorandum. 2019.
Details about Living With Our Star: Enhanced Predictability of GNSS Disturbances | BibTeX data for Living With Our Star: Enhanced Predictability of GNSS Disturbances
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[56]
Machine Learning Retrieval of Jovian and Terrestrial Atmospheres
Michael D. Himes‚ Adam D. Cobb‚ Frank Soboczenski‚ Simone Zorzan‚ Molly D. O’Beirne‚ Atılım Güneş Baydin‚ Yarin Gal‚ Daniel Angerhausen‚ Shawn D. Domagal−Goldman and Giada N. Arney
In American Astronomical Society meeting id. 343.01. Bulletin of the American Astronomical Society‚ Vol. 52‚ No. 1. 2020.
Details about Machine Learning Retrieval of Jovian and Terrestrial Atmospheres | BibTeX data for Machine Learning Retrieval of Jovian and Terrestrial Atmospheres | Link to Machine Learning Retrieval of Jovian and Terrestrial Atmospheres
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[57]
Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures
Jaden J.A. Hastings‚ Aaron C. Bell‚ Timothy Gebhard‚ Jian Gong‚ Atılım Güneş Baydin‚ Matthew Fricke‚ Massimo Mascaro‚ Michael Phillips‚ Kimberly Warren−Rhodes and Nathalie A. Cabrol
In American Geophysical Union (AGU) Fall Meeting‚ December 12–16‚ 2022. 2022.
Details about Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures | BibTeX data for Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures | Link to Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures
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[58]
Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks
Jian Gong‚ Aaron C. Bell‚ Timothy Gebhard‚ Jaden J.A. Hastings‚ Atılım Güneş Baydin‚ Kimberly Warren−Rhodes‚ Michael Phillips‚ Matthew Fricke‚ Nathalie A. Cabrol‚ Scott A. Sandford and Massimo Mascaro
In American Geophysical Union (AGU) Fall Meeting‚ December 12–16‚ 2022. 2022.
Details about Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks | BibTeX data for Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks | Link to Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks
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[59]
Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning
Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Valentina Salvatelli‚ Brad Neuberg‚ Mark Cheung‚ Miho Janvier‚ Meng Jin‚ Yarin Gal‚ Paul Boerner and Atılım Güneş Baydin
In American Geophysical Union (AGU) Fall Meeting‚ December 1–17‚ 2020. 2020.
Details about Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning | BibTeX data for Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning | Link to Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning
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[60]
Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly using Machine Learning
Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Valentina Salvatelli‚ Brad Neuberg‚ Mark Cheung‚ Miho Janvier‚ Meng Jin‚ Yarin Gal‚ Paul Boerner and Atılım Güneş Baydin
In Astronomy & Astrophysics. Vol. 648. Pages A53. 2021.
Details about Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly using Machine Learning | BibTeX data for Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly using Machine Learning | DOI (10.1051/0004-6361/202040051) | Link to Multi−Channel Auto−Calibration for the Atmospheric Imaging Assembly using Machine Learning
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[61]
Nested Compiled Inference for Hierarchical Reinforcement Learning
Tuan Anh Le‚ Atılım Güneş Baydin and Frank Wood
In Neural Information Processing Systems (NIPS) 2016 Workshop on Bayesian Deep Learning‚ Barcelona‚ Spain‚ December 10‚ 2016. 2016.
Details about Nested Compiled Inference for Hierarchical Reinforcement Learning | BibTeX data for Nested Compiled Inference for Hierarchical Reinforcement Learning
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[62]
Neural Network Surrogate Models for Fast Bayesian Inference: Application to Exoplanet Atmospheric Retrieval
Michael D. Himes‚ Joseph Harrington‚ Adam D. Cobb‚ Frank Soboczenski‚ Molly D. O'Beirne‚ Simone Zorzan‚ David C. Wright‚ Zacchaeus Scheffer‚ Shawn D. Domagal−Goldman‚ Giada N. Arney and Atılım Güneş Baydin
In Applications of Statistical Methods and Machine Learning in the Space Sciences‚ 17–21 May 2021. 2021.
Details about Neural Network Surrogate Models for Fast Bayesian Inference: Application to Exoplanet Atmospheric Retrieval | BibTeX data for Neural Network Surrogate Models for Fast Bayesian Inference: Application to Exoplanet Atmospheric Retrieval
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[63]
Observation Strategies and Megaconstellations Impact on Current LEO Population
Giacomo Acciarini‚ Nicola Baresi‚ Christopher Bridges‚ Leonard Felicetti‚ Stephen Hobbs and Atılım Güneş Baydin
In 2nd ESA Near−Earth Object and Debris Detection Conference‚ 24–26 January 2023. 2022.
Details about Observation Strategies and Megaconstellations Impact on Current LEO Population | BibTeX data for Observation Strategies and Megaconstellations Impact on Current LEO Population
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[64]
Onboard cloud detection and atmospheric correction with deep learning emulators
Gonzalo Mateo−Garcìa‚ Cesar Aybar‚ Vít Růžička‚ Giacomo Acciarini‚ Atılım Güneş Baydin‚ Gabriele Meoni‚ Nicolas Longépe‚ James Parr and Luis Gómez−Chova
In International Geoscience and Remote Sensing Symposium‚ July 16 – 21‚ 2023‚ Pasadena‚ CA. 2023.
Details about Onboard cloud detection and atmospheric correction with deep learning emulators | BibTeX data for Onboard cloud detection and atmospheric correction with deep learning emulators | Link to Onboard cloud detection and atmospheric correction with deep learning emulators
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[65]
Online Learning Rate Adaptation with Hypergradient Descent
Atılım Güneş Baydin‚ Robert Cornish‚ David Martínez Rubio‚ Mark Schmidt and Frank Wood
In Sixth International Conference on Learning Representations (ICLR)‚ Vancouver‚ Canada‚ April 30 – May 3‚ 2018. 2018.
Details about Online Learning Rate Adaptation with Hypergradient Descent | BibTeX data for Online Learning Rate Adaptation with Hypergradient Descent
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[66]
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
Kara Lamb‚ Garima Malhotra‚ Athanasios Vlontzos‚ Edward Wagstaff‚ Atılım Güneş Baydin‚ Anahita Bhiwandiwalla‚ Yarin Gal‚ Alfredo Kalaitzis‚ Anthony Reina and Asti Bhatt
In Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Prediction of GNSS Phase Scintillations: A Machine Learning Approach | BibTeX data for Prediction of GNSS Phase Scintillations: A Machine Learning Approach
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[67]
Probabilistic Super−Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Xavier Gitiaux‚ Shane Maloney‚ Anna Jungbluth‚ Carl Shneider‚ Atılım Güneş Baydin‚ Paul J. Wright‚ Yarin Gal‚ Michel Deudon‚ Alfredo Kalaitzis and Andres Munoz−Jaramillo
In Fourth workshop on Bayesian Deep Learning (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Probabilistic Super−Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties | BibTeX data for Probabilistic Super−Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
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[68]
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Andreas Munk‚ Berend Zwartsenberg‚ Adam Ścibior‚ Atılım Güneş Baydin‚ Andrew Stewart‚ Goran Fernlund‚ Anoush Poursartip and Frank Wood
In 38th Conference on Uncertainty in Artificial Intelligence (UAI). 2022.
Details about Probabilistic Surrogate Networks for Simulators with Unbounded Randomness | BibTeX data for Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
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[69]
Seismic savanna: Machine learning for classifying wildlife and behaviours using ground−based vibration field recordings
Alexandre Szenicer‚ Michael Reinwald‚ Ben Moseley‚ Tarje Nissen−Meyer‚ Zacharia Mutinda Muteti‚ Sandy Oduor‚ Alex McDermott−Roberts‚ Atılım Güneş Baydin and Beth Mortimer
In Remote Sensing in Ecology and Conservation. Vol. 8. No. 2. Pages 236–250. 2021.
Details about Seismic savanna: Machine learning for classifying wildlife and behaviours using ground−based vibration field recordings | BibTeX data for Seismic savanna: Machine learning for classifying wildlife and behaviours using ground−based vibration field recordings | DOI (10.1002/rse2.242) | Link to Seismic savanna: Machine learning for classifying wildlife and behaviours using ground−based vibration field recordings
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[70]
Self−supervised Deep Learning for Reducing Data Transmission Needs in Multi−Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory
Valentina Salvatelli‚ Luiz Fernando Guedes dos Santos‚ Mark Cheung‚ Souvik Bose‚ Brad Neuberg‚ Miho Janvier‚ Meng Jin‚ Yarin Gal and Atılım Güneş Baydin
In American Geophysical Union (AGU) Fall Meeting‚ December 13–17‚ 2021. 2021.
Details about Self−supervised Deep Learning for Reducing Data Transmission Needs in Multi−Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory | BibTeX data for Self−supervised Deep Learning for Reducing Data Transmission Needs in Multi−Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory | Link to Self−supervised Deep Learning for Reducing Data Transmission Needs in Multi−Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory
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[71]
Semi−separable Hamiltonian Monte Carlo for inference in Bayesian neural networks
Adam D Cobb‚ Atılım Güneş Baydin‚ Ivan Kiskin‚ Andrew Markham and Stephen Roberts
In Fourth workshop on Bayesian Deep Learning (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Semi−separable Hamiltonian Monte Carlo for inference in Bayesian neural networks | BibTeX data for Semi−separable Hamiltonian Monte Carlo for inference in Bayesian neural networks
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[72]
Simulating Social Networks and Disinformation
Swapneel Mehta‚ Bogdan State‚ Richard Bonneau‚ Jonathan Nagler‚ Philip Torr and Atılım Güneş Baydin
In Misinformation Village co−hosted by MisinfoCon‚ DEFCON 30‚ August 12–13‚ Las Vegas‚ NV‚ USA. 2022.
Details about Simulating Social Networks and Disinformation | BibTeX data for Simulating Social Networks and Disinformation | Link to Simulating Social Networks and Disinformation
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[73]
Simulation−Based Inference for Global Health Decisions
Christian Schroeder de Witt‚ Bradley Gram−Hansen‚ Nantas Nardelli‚ Andrew Gambardella‚ Rob Zinkov‚ Puneet Dokania‚ N. Siddharth‚ Ana Belen Espinosa−Gonzalez‚ Ara Darzi‚ Philip Torr and Atılım Güneş Baydin
In ICML Workshop on Machine Learning for Global Health‚ Thirty−seventh International Conference on Machine Learning (ICML 2020). 2020.
Details about Simulation−Based Inference for Global Health Decisions | BibTeX data for Simulation−Based Inference for Global Health Decisions
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[74]
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles
Bernard Benson‚ Edward Brown‚ Stefano Bonasera‚ Giacomo Acciarini‚ Jorge A. Pérez−Hernández‚ Eric Sutton‚ Moriba K. Jah‚ Christopher Bridges‚ Meng Jin and Atılım Güneş Baydin
In Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021). 2021.
Details about Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles | BibTeX data for Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles
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[75]
Single−Frame Super−Resolution of Solar Magnetograms: Investigating Physics−Based Metrics Based Metrics & Losses
Anna Jungbluth‚ Xavier Gitiaux‚ Shane Maloney‚ Carl Shneider‚ Paul Wright‚ Atılım Güneş Baydin‚ Michel Deudon‚ Alfredo Kalaitzis‚ Yarin Gal and Andres Munoz−Jaramillo
In Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Single−Frame Super−Resolution of Solar Magnetograms: Investigating Physics−Based Metrics Based Metrics & Losses | BibTeX data for Single−Frame Super−Resolution of Solar Magnetograms: Investigating Physics−Based Metrics Based Metrics & Losses
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[76]
Spacecraft Collision Risk Assessment with Probabilistic Programming
Giacomo Acciarini‚ Francesco Pinto‚ Sascha Metz‚ Sarah Boufelja‚ Sylvester Kaczmarek‚ Klaus Merz‚ José A. Martinez−Heras‚ Francesca Letizia‚ Christopher Bridges and Atılım Güneş Baydin
In Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)‚ Vancouver‚ Canada. 2020.
Details about Spacecraft Collision Risk Assessment with Probabilistic Programming | BibTeX data for Spacecraft Collision Risk Assessment with Probabilistic Programming
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[77]
Studying Solar Energetic Particles and Their Seed Population Using Surrogate Models
Bala Poduval‚ Atılım Güneş Baydin and Nathan Schwadron
In Machine Learning for Space Sciences workshop‚ 43rd Committee on Space Research (COSPAR) Scientific Assembly‚ Sydney‚ Australia. 2021.
Details about Studying Solar Energetic Particles and Their Seed Population Using Surrogate Models | BibTeX data for Studying Solar Energetic Particles and Their Seed Population Using Surrogate Models
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[78]
Super−resolution Maps of Solar Magnetic Field Covering 40 Years of Space Weather Events
Xavier Gitiaux‚ Anna Jungbluth‚ Shane Maloney‚ Carl Shneider‚ Atılım Güneş Baydin‚ Andrés Muñoz−Jaramillo and Paul Wright
NASA Frontier Development Lab Technical Memorandum. 2019.
Details about Super−resolution Maps of Solar Magnetic Field Covering 40 Years of Space Weather Events | BibTeX data for Super−resolution Maps of Solar Magnetic Field Covering 40 Years of Space Weather Events
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[79]
Super−resolution of Solar Magnetograms
Paul James Wright‚ Xavier Gitiaux‚ Anna Jungbluth‚ Shane Maloney‚ Carl Shneider‚ Alfredo Kalaitzis‚ Atılım Güneş Baydin‚ Michel Deudon‚ Yarin Gal and Andres Munoz−Jaramillo
In American Geophysical Union (AGU) Fall Meeting‚ December 1–17‚ 2020. 2020.
Details about Super−resolution of Solar Magnetograms | BibTeX data for Super−resolution of Solar Magnetograms | Link to Super−resolution of Solar Magnetograms
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[80]
Super−resolution of MDI (and GONG) Magnetograms
Paul Wright‚ Xavier Gitiaux‚ Anna Jungbluth‚ Shane Maloney‚ Carl Shneider‚ Alfredo Kalaitzis‚ Michel Deudon‚ Atılım Güneş Baydin‚ Yarin Gal and Andres Munoz−Jaramillo
In 50th Anniversary Meeting of the Solar Physics Division (SPD) of the American Astronomical Society (AAS). 2020.
Details about Super−resolution of MDI (and GONG) Magnetograms | BibTeX data for Super−resolution of MDI (and GONG) Magnetograms | Link to Super−resolution of MDI (and GONG) Magnetograms
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[81]
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin‚ Ciaran M. Gilligan−Lee‚ Alessya Visnjic‚ Siddha Ganju‚ Dava Newman‚ Sujoy Ganguly‚ Danny Lange‚ Atılım Güneş Baydin‚ Amit Sharma‚ Adam Gibson‚ Stephan Zheng‚ Yarin Gal‚ Eric P. Xing‚ Chris Mattmann and James Parr
In Nature Communications. Vol. 13. No. 6039. 2022.
Details about Technology Readiness Levels for Machine Learning Systems | BibTeX data for Technology Readiness Levels for Machine Learning Systems | DOI (10.1038/s41467-022-33128-9) | Link to Technology Readiness Levels for Machine Learning Systems
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[82]
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin‚ Ciaran M. Gilligan−Lee‚ Alessya Visnjic‚ Siddha Ganju‚ Dava Newman‚ Sujoy Ganguly‚ Danny Lange‚ Atılım Güneş Baydin‚ Amit Sharma‚ Adam Gibson‚ Yarin Gal‚ Eric P. Xing‚ Chris Mattmann and James Parr
In Nature Communications. 2022 (to appear).
Details about Technology Readiness Levels for Machine Learning Systems | BibTeX data for Technology Readiness Levels for Machine Learning Systems
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[83]
Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods
Michael D. Himes‚ Josepth Harrington and Atılım Güneş Baydin
In The Planetary Science Journal. Vol. 4. No. 74. 2023.
Details about Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods | BibTeX data for Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods | DOI (10.3847/PSJ/acc939) | Link to Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods
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[84]
Toward Machine Learning Optimization of Experimental Design
Atılım Güneş Baydin‚ Kyle Cranmer‚ Pablo de Castro Manzano‚ Christophe Delaere‚ Denis Derkach‚ Julien Donini‚ Tommaso Dorigo‚ Andrea Giammanco‚ Jan Kieseler‚ Lukas Layer‚ Gilles Louppe‚ Fedor Ratnikov‚ Giles C. Strong‚ Mia Tosi‚ Andrey Ustyuzhanin‚ Pietro Vischia and Hevjin Yarar
In Nuclear Physics News. Vol. 31. No. 1. Pages 25–28. 2021.
Details about Toward Machine Learning Optimization of Experimental Design | BibTeX data for Toward Machine Learning Optimization of Experimental Design | DOI (10.1080/10619127.2021.1881364) | Link to Toward Machine Learning Optimization of Experimental Design
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[85]
Toward the End−to−End Optimization of Particle Physics Instruments with Differentiable Programming
Tommaso Dorigo‚ Andrea Giammanco‚ Pietro Vischia‚ Max Aehle‚ Mateusz Bawaj‚ Alexey Boldyrev‚ Pablo de Castro Manzano‚ Denis Derkach‚ Julien Donini‚ Auralee Edelen‚ Federica Fanzago‚ Nicolas R. Gauger‚ Christian Glaser‚ Atılım Güneş Baydin‚ Lukas Heinrich‚ Ralf Keidel‚ Jan Kieseler‚ Claudius Krause‚ Maxime Lagrange‚ Max Lamparth‚ Lukas Layer‚ Gernot Maier‚ Federico Nardi‚ Helge E.S. Pettersen‚ Alberto Ramos‚ Fedor Ratnikov‚ Dieter Röhrich‚ Roberto Ruiz de Austri‚ Pablo Martínez Ruiz del Árbol‚ Oleg Savchenko‚ Nathan Simpson‚ Giles C. Strong‚ Angela Taliercio‚ Mia Tosi‚ Andrey Ustyuzhanin and Haitham Zaraket
In Reviews in Physics. Pages 100085. 2023.
Details about Toward the End−to−End Optimization of Particle Physics Instruments with Differentiable Programming | BibTeX data for Toward the End−to−End Optimization of Particle Physics Instruments with Differentiable Programming | DOI (10.1016/j.revip.2023.100085) | Link to Toward the End−to−End Optimization of Particle Physics Instruments with Differentiable Programming
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[86]
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
Francesco Pinto‚ Giacomo Acciarini‚ Sascha Metz‚ Sarah Boufelja‚ Sylvester Kaczmarek‚ Klaus Merz‚ José A. Martinez−Heras‚ Francesca Letizia‚ Christopher Bridges and Atılım Güneş Baydin
In AI for Earth Sciences Workshop at NeurIPS 2020‚ Vancouver‚ Canada. 2020.
Details about Towards Automated Satellite Conjunction Management with Bayesian Deep Learning | BibTeX data for Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
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[87]
Towards Global Flood Mapping Onboard Low Cost Satellites with Machine Learning
Gonzalo Mateo−Garcia‚ Joshua Veitch−Michaelis‚ Lewis Smith‚ Silviu Oprea‚ Guy Schumann‚ Yarin Gal‚ Atılım Güneş Baydin and Dietmar Backes
In Scientific Reports. Vol. 11. No. 7249. 2021.
Details about Towards Global Flood Mapping Onboard Low Cost Satellites with Machine Learning | BibTeX data for Towards Global Flood Mapping Onboard Low Cost Satellites with Machine Learning | DOI (10.1038/s41598-021-86650-z) | Link to Towards Global Flood Mapping Onboard Low Cost Satellites with Machine Learning
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[88]
Tricks from Deep Learning
Atılım Güneş Baydin‚ Barak A. Pearlmutter and Jeffrey Mark Siskind
In 7th International Conference on Algorithmic Differentiation‚ Christ Church Oxford‚ UK‚ September 12–15‚ 2016. 2016.
Details about Tricks from Deep Learning | BibTeX data for Tricks from Deep Learning
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[89]
Usability of Probabilistic Programming Languages
Alan Blackwell‚ Tobias Kohn‚ Martin Erwig‚ Atılım Güneş Baydin‚ Luke Church‚ James Geddes‚ Andy Gordon‚ Maria Gorinova‚ Bradley Gram−Hansen‚ Neil Lawrence‚ Vikash Mansinghka‚ Brooks Paige‚ Tomas Petricek‚ Diana Robinson‚ Advait Sarkar and Oliver Strickson
In Psychology of Programming Interest Group Annual Workshop (PPIG 2019)‚ Newcastle‚ UK‚ 28–30 August 2019. 2019.
Details about Usability of Probabilistic Programming Languages | BibTeX data for Usability of Probabilistic Programming Languages
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[90]
Using Synthetic Data to Train Neural Networks is Model−Based Reasoning
Tuan Anh Le‚ Atılım Güneş Baydin‚ Robert Zinkov and Frank Wood
In 30th International Joint Conference on Neural Networks‚ Anchorage‚ AK‚ USA‚ May 14–19‚ 2017. 2017.
Details about Using Synthetic Data to Train Neural Networks is Model−Based Reasoning | BibTeX data for Using Synthetic Data to Train Neural Networks is Model−Based Reasoning
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[91]
Using U−Nets to create high−fidelity virtual observations of the solar corona
Valentina Salvatelli‚ Souvik Bose‚ Brad Neuberg‚ Luiz F. Guedes dos Santos‚ Mark Cheung‚ Miho Janvier‚ Atılım Güneş Baydin‚ Yarin Gal and Meng Jin
In Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019)‚ Vancouver‚ Canada. 2019.
Details about Using U−Nets to create high−fidelity virtual observations of the solar corona | BibTeX data for Using U−Nets to create high−fidelity virtual observations of the solar corona
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[92]
EXO−ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres
Aditya Chopra‚ Aaron Bell‚ William Fawcett‚ Rodd Talebi‚ Daniel Angerhausen‚ Atılım Güneş Baydin‚ Anamaria Berea‚ Nathalie A. Cabrol‚ Chris Kempes and Massimo Mascaro
In Astrobiology Science Conference (AbSciCon 2019)‚ Bellevue‚ Washington‚ June 24–28‚ 2019. 2019.
Details about EXO−ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres | BibTeX data for EXO−ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres | Link to EXO−ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres
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[93]
EXO−ATMOS: A scalable grid of hypothetical planetary atmospheres
Aditya Chopra‚ Aaron Bell‚ William Fawcett‚ Rodd Talebi‚ Daniel Angerhausen‚ Atılım Güneş Baydin‚ Anamaria Berea‚ Nathalie A. Cabrol‚ Chris Kempes and Massimo Mascaro
In Europlanet Science Congress 2020. Vol. 14. Pages EPSC2020−664. 2020.
Details about EXO−ATMOS: A scalable grid of hypothetical planetary atmospheres | BibTeX data for EXO−ATMOS: A scalable grid of hypothetical planetary atmospheres | Link to EXO−ATMOS: A scalable grid of hypothetical planetary atmospheres
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[94]
INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval
Frank Soboczenski‚ Michael D. Himes‚ Molly D. O’Beirne‚ Simone Zorzan‚ Atılım Güneş Baydin‚ Adam D. Cobb‚ Yarin Gal‚ Daniel Angerhausen‚ Massimo Mascaro‚ Geronimo Villanueva‚ Shawn D. Domagal−Goldman and Giada N. Arney
In Second AI and Data Science Workshop for Earth and Space Sciences‚ Jet Propulsion Laboratory (NASA JPL)‚ Pasadena‚ CA‚ United States‚ March 24–26‚ 2020. 2020.
Details about INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval | BibTeX data for INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval | Link to INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval
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[95]
KL Guided Domain Adaptation
Tuan Nguyen‚ Toan Tran‚ Yarin Gal‚ Philip H.S. Torr and Atılım Güneş Baydin
In Tenth International Conference on Learning Representations (ICLR). 2022.
Details about KL Guided Domain Adaptation | BibTeX data for KL Guided Domain Adaptation