Evolution of central pattern generators for the control of a five-link planar bipedal walking mechanism - Atilim Günes Baydin (CoRR, 2008)

CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation. - Atilim Günes Baydin; Ramón López de Mántaras; Simeon J. Simoff; Carles Sierra (ICCBR, 2011)

Evolution of ideas: A novel memetic algorithm based on semantic networks. - Atilim Günes Baydin; Ramón López de Mántaras (IEEE Congress on Evolutionary Computation, 2012)

Automated Generation of Cross-Domain Analogies via Evolutionary Computation. - Atilim Günes Baydin; Ramón López de Mántaras; Santiago Ontañón (ICCC, 2012)

Evolution of central pattern generators for the control of a five-link bipedal walking mechanism. - Atilim Günes Baydin (Paladyn J. Behav. Robotics, 2012)

City versus wetland: Predicting urban growth in the Vecht area with a cellular automaton model - Melek Tendurus; Atilim Günes Baydin; Marieke A. Eleveld; Alison J. Gilbert (CoRR, 2013)

A semantic network-based evolutionary algorithm for modeling memetic evolution and creativity. - Atilim Günes Baydin; Ramón López de Mántaras; Santiago Ontañón (CoRR, 2014)

Automatic Differentiation of Algorithms for Machine Learning. - Atilim Gunes Baydin; Barak A. Pearlmutter (CoRR, 2014)

An Analysis of Publication Venues for Automatic Differentiation Research. - Atilim Gunes Baydin; Barak A. Pearlmutter (CoRR, 2014)

Automatic differentiation in machine learning: a survey. - Atilim Gunes Baydin; Barak A. Pearlmutter; Alexey Andreyevich Radul (CoRR, 2015)

DiffSharp: Automatic Differentiation Library. - Atilim Gunes Baydin; Barak A. Pearlmutter; Jeffrey Mark Siskind (CoRR, 2015)

A semantic network-based evolutionary algorithm for computational creativity. - Atilim Günes Baydin; Ramón López de Mántaras; Santiago Ontañón (Evol. Intell., 2015)

DiffSharp: An AD Library for .NET Languages. - Atilim Günes Baydin; Barak A. Pearlmutter; Jeffrey Mark Siskind (CoRR, 2016)

Tricks from Deep Learning. - Atilim Günes Baydin; Barak A. Pearlmutter; Jeffrey Mark Siskind (CoRR, 2016)

Inference Compilation and Universal Probabilistic Programming. - Tuan Anh Le 001; Atilim Gunes Baydin; Frank D. Wood (AISTATS, 2017)

Using synthetic data to train neural networks is model-based reasoning. - Tuan Anh Le 001; Atilim Günes Baydin; Robert Zinkov; Frank D. Wood (IJCNN, 2017)

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. - Mario Lezcano Casado; Atilim Gunes Baydin; David Martínez-Rubio; Tuan Anh Le 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 2017)

Automatic Differentiation in Machine Learning: a Survey. - Atilim Gunes Baydin; Barak A. Pearlmutter; Alexey Andreyevich Radul; Jeffrey Mark Siskind (J. Mach. Learn. Res., 2017)

Online Learning Rate Adaptation with Hypergradient Descent. - Atilim Gunes Baydin; Robert Cornish; David Martínez-Rubio; Mark Schmidt 001; Frank Wood (ICLR (Poster), 2018)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lukas Heinrich; Wahid Bhimji; Bradley Gram-Hansen; Gilles Louppe; Lei Shao; Prabhat; Kyle Cranmer; Frank D. Wood (CoRR, 2018)

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval. - Frank Soboczenski; Michael D. Himes; Molly D. O'Beirne; Simone Zorzan; Atilim Gunes Baydin; Adam D. Cobb; Daniel Angerhausen; Giada N. Arney; Shawn D. Domagal-Goldman (CoRR, 2018)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Saeid Naderiparizi; Andreas Munk 001; Jialin Liu 002; Bradley Gram-Hansen; Gilles Louppe; Lawrence Meadows; Philip H. S. Torr; Victor W. Lee; et al. (NeurIPS, 2019)

Usability of Probabilistic Programming Languages. - Alan F. Blackwell; Luke Church; Martin Erwig; James Geddes; Andy Gordon 001; Maria I. Gorinova 001; Atilim Gunes Baydin; Bradley Gram-Hansen; Tobias Kohn; Neil D. Lawrence; Vikash Mansinghka 001; Brooks Paige; et al. (PPIG, 2019)

Etalumis: bringing probabilistic programming to scientific simulators at scale. - Atilim Günes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Lawrence Meadows; Jialin Liu 002; Andreas Munk 001; Saeid Naderiparizi; Bradley Gram-Hansen; Gilles Louppe; Mingfei Ma; Xiaohui Zhao; et al. (SC, 2019)

Alpha MAML: Adaptive Model-Agnostic Meta-Learning. - Harkirat Singh Behl; Atilim Günes Baydin; Philip H. S. Torr (CoRR, 2019)

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. - Adam D. Cobb; Michael D. Himes; Frank Soboczenski; Simone Zorzan; Molly D. O'Beirne; Atilim Günes Baydin; Yarin Gal; Shawn D. Domagal-Goldman; Giada N. Arney; Daniel Angerhausen (CoRR, 2019)

Hijacking Malaria Simulators with Probabilistic Programming. - Bradley Gram-Hansen; Christian Schröder de Witt; Tom Rainforth; Philip H. S. Torr; Yee Whye Teh; Atilim Günes Baydin (CoRR, 2019)

Prediction of GNSS Phase Scintillations: A Machine Learning Approach. - Kara Lamb; Garima Malhotra; Athanasios Vlontzos; Edward Wagstaff; Atilim Günes Baydin; Anahita Bhiwandiwalla; Yarin Gal; Alfredo Kalaitzis; Anthony Reina; Asti Bhatt (CoRR, 2019)

Flood Detection On Low Cost Orbital Hardware. - Gonzalo Mateo-Garcia; Silviu Oprea; Lewis Smith; Josh Veitch-Michaelis; Guy Schumann; Yarin Gal; Atilim Günes Baydin; Dietmar Backes (CoRR, 2019)

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder. - Kara Lamb; Garima Malhotra; Athanasios Vlontzos; Edward Wagstaff; Atilim Günes Baydin; Anahita Bhiwandiwalla; Yarin Gal; Alfredo Kalaitzis; Anthony Reina; Asti Bhatt (CoRR, 2019)

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo. - Adam D. Cobb; Atilim Günes Baydin; Andrew Markham; Stephen J. Roberts (CoRR, 2019)

Deep Probabilistic Surrogate Networks for Universal Simulator Approximation. - Andreas Munk 001; Adam Scibior; Atilim Günes Baydin; Andrew Stewart; Goran Fernlund; Anoush Poursartip; Frank Wood (CoRR, 2019)

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties. - Xavier Gitiaux; Shane A. Maloney; Anna Jungbluth; Carl Shneider; Paul J. Wright; Atilim Günes Baydin; Michel Deudon; Yarin Gal; Alfredo Kalaitzis; Andrés Muñoz-Jaramillo (CoRR, 2019)

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses. - Anna Jungbluth; Xavier Gitiaux; Shane A. Maloney; Carl Shneider; Paul J. Wright; Alfredo Kalaitzis; Michel Deudon; Atilim Günes Baydin; Yarin Gal; Andrés Muñoz-Jaramillo (CoRR, 2019)

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona. - Valentina Salvatelli; Souvik Bose; Brad Neuberg; Luiz F. G. dos Santos; Mark C. M. Cheung; Miho Janvier; Atilim Gunes Baydin; Yarin Gal; Meng Jin (CoRR, 2019)

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning. - Brad Neuberg; Souvik Bose; Valentina Salvatelli; Luiz F. G. dos Santos; Mark C. M. Cheung; Miho Janvier; Atilim Gunes Baydin; Yarin Gal; Meng Jin (CoRR, 2019)

Transflow Learning: Repurposing Flow Models Without Retraining. - Andrew Gambardella; Atilim Günes Baydin; Philip H. S. Torr (CoRR, 2019)

AutoSimulate: (Quickly) Learning Synthetic Data Generation. - Harkirat Singh Behl; Atilim Günes Baydin; Ran Gal; Philip H. S. Torr; Vibhav Vineet (ECCV (22), 2020)

Black-Box Optimization with Local Generative Surrogates. - Sergey Shirobokov; Vladislav Belavin; Michael Kagan; Andrey Ustyuzhanin; Atilim Gunes Baydin (NeurIPS, 2020)

Differentiating the Black-Box: Optimization with Local Generative Surrogates. - Sergey Shirobokov; Vladislav Belavin; Michael Kagan; Andrey Ustyuzhanin; Atilim Günes Baydin (CoRR, 2020)

Simulation-Based Inference for Global Health Decisions. - Christian Schröder de Witt; Bradley Gram-Hansen; Nantas Nardelli; Andrew Gambardella; Robert Zinkov; Puneet K. Dokania; N. Siddharth 001; Ana Belen Espinosa-Gonzalez; Ara Darzi; Philip H. S. Torr; Atilim Günes Baydin (CoRR, 2020)

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 001; Atilim Günes Baydin (CoRR, 2020)

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 001; Atilim Günes Baydin (CoRR, 2020)

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning. - Luiz F. G. dos Santos; Souvik Bose; Valentina Salvatelli; Brad Neuberg; Mark C. M. Cheung; Miho Janvier; Meng Jin; Yarin Gal; Paul Boerner; Atilim Günes Baydin (CoRR, 2020)

Converter software to calibrate and super-resolve solar magnetograms. - Andrés Muñoz-Jaramillo; Xavier Gitiaux; Anna Jungbluth; Shane A. Maloney; Carl Shneider; Paul J. Wright; Michel Deudon; Alfredo Kalaitzis; Atilim Günes Baydin; Yarin Gal (Zenodo, 2020)

Domain Invariant Representation Learning with Domain Density Transformations. - A. Tuan Nguyen; Toan Tran 003; Yarin Gal; Atilim Gunes Baydin (NeurIPS, 2021)

Technology Readiness Levels for Machine Learning Systems. - Alexander Lavin; Ciarán M. Gilligan-Lee; Alessya Visnjic; Siddha Ganju; Dava J. Newman; Sujoy Ganguly; Danny Lange; Atilim Günes Baydin; Amit Sharma 007; Adam Gibson; Yarin Gal; Eric P. Xing; et al. (CoRR, 2021)

Detecting and Quantifying Malicious Activity with Simulation-based Inference. - Andrew Gambardella; Bogdan State; Naeemullah Khan; Leo Tsourides; Philip H. S. Torr; Atilim Günes Baydin (CoRR, 2021)

Simulation Intelligence: Towards a New Generation of Scientific Methods. - Alexander Lavin; Hector Zenil; Brooks Paige; David Krakauer; Justin Gottschlich; Tim Mattson; Anima Anandkumar; Sanjay Choudry; Kamil Rocki; Atilim Günes Baydin; Carina Prunkl; Olexandr Isayev; et al. (CoRR, 2021)

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 001; Meng Jin; Atilim Günes Baydin (CoRR, 2021)

Amortized Rejection Sampling in Universal Probabilistic Programming. - Saeid Naderiparizi; Adam Scibior; Andreas Munk 001; Mehrdad Ghadiri; Atilim Gunes Baydin; Bradley J. Gram-Hansen; Christian A. Schröder de Witt; Robert Zinkov; Philip H. S. Torr; Tom Rainforth; Yee Whye Teh; Frank Wood (AISTATS, 2022)

KL Guided Domain Adaptation. - A. Tuan Nguyen; Toan Tran 003; Yarin Gal; Philip H. S. Torr; Atilim Gunes Baydin (ICLR, 2022)

Attention for Inference Compilation. - William Harvey 002; Andreas Munk 001; Atilim Günes Baydin; Alexander Bergholm; Frank Wood (SIMULTECH, 2022)

Probabilistic surrogate networks for simulators with unbounded randomness. - Andreas Munk 001; Berend Zwartsenberg; Adam Scibior; Atilim Günes Baydin; Andrew Stewart; Goran Fernlund; Anoush Poursartip; Frank Wood (UAI, 2022)

Gradients without Backpropagation. - Atilim Günes Baydin; Barak A. Pearlmutter; Don Syme; Frank Wood; Philip H. S. Torr (CoRR, 2022)

Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation. - Valentina Salvatelli; Luiz F. G. dos Santos; Souvik Bose; Brad Neuberg; Mark C. M. Cheung; Miho Janvier; Meng Jin; Yarin Gal; Atilim Gunes Baydin (CoRR, 2022)

Managing AI Risks in an Era of Rapid Progress. - Yoshua Bengio; Geoffrey E. Hinton; Andrew Yao; Dawn Song; Pieter Abbeel; Yuval Noah Harari; Ya-Qin Zhang; Lan Xue; Shai Shalev-Shwartz; Gillian K. Hadfield; Jeff Clune; Tegan Maharaj; et al. (CoRR, 2023)

High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery. - Shreshth A. Malik; James Walsh 008; Giacomo Acciarini; Thomas E. Berger; Atilim Günes Baydin (CoRR, 2023)

Closing the Gap Between SGP4 and High-Precision Propagation via Differentiable Programming. - Giacomo Acciarini; Atilim Günes Baydin; Dario Izzo (CoRR, 2024)

Second-Order Forward-Mode Automatic Differentiation for Optimization. - Adam D. Cobb; Atilim Günes Baydin; Barak A. Pearlmutter; Susmit Jha (CoRR, 2024)

A Foundation Model for the Solar Dynamics Observatory. - James Walsh 008; Daniel G. Gass; Raúl Ramos-Pollán; Paul J. Wright; Richard Galvez; Noah Kasmanoff; Jason Naradowsky; Anne Spalding; James Parr; Atilim Günes Baydin (CoRR, 2024)

Probabilistic Forecasting of Radiation Exposure for Spaceflight. - Rutuja Gurav; Elena Massara; Xiaomei Song; Kimberly Sinclair; Edward Brown; Matt J. Kusner; Bala Poduval; Atilim Gunes Baydin (CoRR, 2024)

Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept. - Sam A. Scivier; Tarje Nissen-Meyer; Paula Koelemeijer; Atilim Günes Baydin (CoRR, 2024)