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2020 – today
- 2021
- [i69]Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr:
Technology Readiness Levels for Machine Learning Systems. CoRR abs/2101.03989 (2021) - [i68]Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When To Fix It. CoRR abs/2101.11665 (2021) - [i67]Panagiotis Tigas, Téo Bloch, Vishal Upendran, Banafsheh Ferdoushi, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt:
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. CoRR abs/2102.01447 (2021) - [i66]A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Günes Baydin:
Domain Invariant Representation Learning with Domain Density Transformations. CoRR abs/2102.05082 (2021) - [i65]Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal:
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR abs/2102.11409 (2021) - [i64]Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. CoRR abs/2102.11582 (2021) - [i63]Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar:
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. CoRR abs/2102.12560 (2021) - 2020
- [j1]Iryna Korshunova, Yarin Gal, Arthur Gretton
, Joni Dambre
:
Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing 416: 305-309 (2020) - [c34]Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning. AISTATS 2020: 1352-1362 - [c33]Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal:
BayesOpt Adversarial Attack. ICLR 2020 - [c32]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. ICLR 2020 - [c31]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020: 3145-3153 - [c30]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. ICML 2020: 8286-8294 - [c29]Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal:
Uncertainty Estimation Using a Single Deep Deterministic Neural Network. ICML 2020: 9690-9700 - [c28]Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. ICML 2020: 11214-11224 - [c27]Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska:
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. ICRA 2020: 7344-7350 - [c26]Marc Rußwurm, Mohsin Ali, Xiaoxiang Zhu, Yarin Gal, Marco Körner:
Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models. IGARSS 2020: 7025-7028 - [c25]Sebastian Farquhar, Lewis Smith, Yarin Gal:
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. NeurIPS 2020 - [c24]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS 2020 - [c23]Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. NeurIPS 2020 - [c22]Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal:
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS 2020 - [i62]Sebastian Farquhar, Lewis Smith, Yarin Gal:
Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks. CoRR abs/2002.03704 (2020) - [i61]Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal:
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. CoRR abs/2003.02037 (2020) - [i60]Amy Zhang
, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup:
Invariant Causal Prediction for Block MDPs. CoRR abs/2003.06016 (2020) - [i59]Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra:
Baryons from Mesons: A Machine Learning Perspective. CoRR abs/2003.10445 (2020) - [i58]Andreas Kirsch, Clare Lyle, Yarin Gal:
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR abs/2003.12537 (2020) - [i57]Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk:
Capsule Networks - A Probabilistic Perspective. CoRR abs/2004.03553 (2020) - [i56]Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy:
On the Benefits of Invariance in Neural Networks. CoRR abs/2005.00178 (2020) - [i55]Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel:
Uncertainty Evaluation Metric for Brain Tumour Segmentation. CoRR abs/2005.14262 (2020) - [i54]Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal:
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search. CoRR abs/2006.04492 (2020) - [i53]Tim Z. Xiao, Aidan N. Gomez, Yarin Gal:
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers. CoRR abs/2006.08344 (2020) - [i52]Amy Zhang
, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR abs/2006.10742 (2020) - [i51]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR abs/2006.14911 (2020) - [i50]Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal:
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR abs/2007.00163 (2020) - [i49]Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas D. Lane, Yarin Gal:
Single Shot Structured Pruning Before Training. CoRR abs/2007.00389 (2020) - [i48]Pascal Notin, Aidan N. Gomez, Joanna Yoo, Yarin Gal:
SliceOut: Training Transformers and CNNs faster while using less memory. CoRR abs/2007.10909 (2020) - [i47]Mrinank Sharma, Sören Mindermann, Jan Markus Brauner, Gavin Leech, Anna B. Stephenson, Tomas Gavenciak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal:
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission. CoRR abs/2007.13454 (2020) - [i46]Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal:
Interlocking Backpropagation: Improving depthwise model-parallelism. CoRR abs/2010.04116 (2020) - [i45]Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz Rodríguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi:
Physics-informed GANs for Coastal Flood Visualization. CoRR abs/2010.08103 (2020) - [i44]Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk:
A Bayesian Perspective on Training Speed and Model Selection. CoRR abs/2010.14499 (2020) - [i43]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. CoRR abs/2011.00415 (2020) - [i42]Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth:
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR abs/2011.00515 (2020) - [i41]Mizu Nishikawa-Toomey, Lewis Smith, Yarin Gal:
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders. CoRR abs/2011.08714 (2020) - [i40]Jishnu Mukhoti, Puneet K. Dokania, Philip H. S. Torr, Yarin Gal:
On Batch Normalisation for Approximate Bayesian Inference. CoRR abs/2012.13220 (2020) - [i39]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:
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning. CoRR abs/2012.14023 (2020)
2010 – 2019
- 2019
- [c21]Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: a neural process for exchangeable labelled data. ESANN 2019 - [c20]Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal:
An Empirical study of Binary Neural Networks' Optimisation. ICLR (Poster) 2019 - [c19]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS 2019: 7024-7035 - [i38]Sebastian Farquhar, Yarin Gal:
A Unifying Bayesian View of Continual Learning. CoRR abs/1902.06494 (2019) - [i37]Sebastian Farquhar, Yarin Gal:
Differentially Private Continual Learning. CoRR abs/1902.06497 (2019) - [i36]Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford
, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons
, Rebecca Smethurst, Darryl Wright:
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning. CoRR abs/1905.07424 (2019) - [i35]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:
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. CoRR abs/1905.10659 (2019) - [i34]Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton:
Learning Sparse Networks Using Targeted Dropout. CoRR abs/1905.13678 (2019) - [i33]Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva:
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. CoRR abs/1906.04813 (2019) - [i32]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR abs/1906.08158 (2019) - [i31]Sebastian Farquhar, Michael A. Osborne, Yarin Gal:
Radial Bayesian Neural Networks: Robust Variational Inference In Big Models. CoRR abs/1907.00865 (2019) - [i30]Zachary Kenton, Angelos Filos, Owain Evans, Yarin Gal:
Generalizing from a few environments in safety-critical reinforcement learning. CoRR abs/1907.01475 (2019) - [i29]Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, Marta Kwiatkowska:
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. CoRR abs/1909.09884 (2019) - [i28]Kara Lamb, Garima Malhotra
, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt:
Prediction of GNSS Phase Scintillations: A Machine Learning Approach. CoRR abs/1910.01570 (2019) - [i27]Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith, Josh Veitch-Michaelis
, Guy Schumann, Yarin Gal, Atilim Günes Baydin, Dietmar Backes:
Flood Detection On Low Cost Orbital Hardware. CoRR abs/1910.03019 (2019) - [i26]Kara Lamb, Garima Malhotra
, Athanasios Vlontzos, Edward Wagstaff, Atilim Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt:
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder. CoRR abs/1910.03085 (2019) - [i25]Chelsea Sidrane, Dylan J. Fitzpatrick, Andrew Annex, Diane O'Donoghue, Yarin Gal, Piotr Bilinski:
Machine Learning for Generalizable Prediction of Flood Susceptibility. CoRR abs/1910.06521 (2019) - [i24]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. CoRR abs/1910.08348 (2019) - [i23]Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth, Carl Shneider
, Paul J. Wright
, Atilim Günes Baydin, Michel Deudon, Yarin Gal, Alfredo Kalaitzis, Andrés Muñoz-Jaramillo:
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties. CoRR abs/1911.01486 (2019) - [i22]Anna Jungbluth, Xavier Gitiaux, Shane A. Maloney, Carl Shneider
, Paul J. Wright
, Alfredo Kalaitzis, Michel Deudon, Atilim Günes Baydin, Yarin Gal, Andrés Muñoz-Jaramillo:
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses. CoRR abs/1911.01490 (2019) - [i21]Valentina Salvatelli, Souvik Bose, Brad Neuberg, Luiz F. G. dos Santos
, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin:
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona. CoRR abs/1911.04006 (2019) - [i20]Brad Neuberg, Souvik Bose, Valentina Salvatelli, Luiz F. G. dos Santos
, Mark C. M. Cheung, Miho Janvier, Atilim Gunes Baydin, Yarin Gal, Meng Jin:
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning. CoRR abs/1911.04008 (2019) - [i19]Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva:
Adversarial recovery of agent rewards from latent spaces of the limit order book. CoRR abs/1912.04242 (2019) - [i18]Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal:
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. CoRR abs/1912.10481 (2019) - 2018
- [c18]Alex Kendall, Yarin Gal, Roberto Cipolla
:
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CVPR 2018: 7482-7491 - [c17]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML 2018: 2616-2625 - [c16]Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre:
BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS 2018: 7190-7198 - [c15]Lewis Smith, Yarin Gal:
Understanding Measures of Uncertainty for Adversarial Example Detection. UAI 2018: 560-569 - [i17]Lewis Smith, Yarin Gal:
Understanding Measures of Uncertainty for Adversarial Example Detection. CoRR abs/1803.08533 (2018) - [i16]Adam D. Cobb, Stephen J. Roberts, Yarin Gal:
Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR abs/1805.03901 (2018) - [i15]Sebastian Farquhar, Yarin Gal:
Towards Robust Evaluations of Continual Learning. CoRR abs/1805.09733 (2018) - [i14]Yarin Gal, Lewis Smith:
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study. CoRR abs/1806.00667 (2018) - [i13]Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava:
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR abs/1806.04854 (2018) - [i12]Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal:
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control. CoRR abs/1811.06817 (2018) - [i11]Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal:
On the Importance of Strong Baselines in Bayesian Deep Learning. CoRR abs/1811.09385 (2018) - [i10]Jishnu Mukhoti, Yarin Gal:
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation. CoRR abs/1811.12709 (2018) - 2017
- [c14]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. ICML 2017: 1183-1192 - [c13]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML 2017: 2052-2061 - [c12]Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller:
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI 2017: 4745-4753 - [c11]Yarin Gal, Jiri Hron, Alex Kendall:
Concrete Dropout. NIPS 2017: 3581-3590 - [c10]Alex Kendall, Yarin Gal:
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? NIPS 2017: 5574-5584 - [c9]Piotr Dabkowski, Yarin Gal:
Real Time Image Saliency for Black Box Classifiers. NIPS 2017: 6967-6976 - [i9]Yarin Gal, Riashat Islam, Zoubin Ghahramani:
Deep Bayesian Active Learning with Image Data. CoRR abs/1703.02910 (2017) - [i8]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR abs/1703.02914 (2017) - [i7]Alex Kendall, Yarin Gal:
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? CoRR abs/1703.04977 (2017) - [i6]Alex Kendall, Yarin Gal, Roberto Cipolla:
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CoRR abs/1705.07115 (2017) - [i5]Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal:
Vprop: Variational Inference using RMSprop. CoRR abs/1712.01038 (2017) - 2016
- [c8]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML 2016: 1050-1059 - [c7]Yarin Gal, Zoubin Ghahramani:
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. NIPS 2016: 1019-1027 - 2015
- [c6]Yarin Gal, Yutian Chen, Zoubin Ghahramani:
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data. ICML 2015: 645-654 - [c5]Yarin Gal, Richard Turner:
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. ICML 2015: 655-664 - [i4]Yarin Gal, Zoubin Ghahramani:
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. CoRR abs/1506.02142 (2015) - [i3]Yarin Gal, Zoubin Ghahramani:
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. CoRR abs/1506.02158 (2015) - 2014
- [c4]Yarin Gal, Zoubin Ghahramani:
Pitfalls in the use of Parallel Inference for the Dirichlet Process. ICML 2014: 208-216 - [c3]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS 2014: 3257-3265 - [i2]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR abs/1402.1389 (2014) - [i1]Yarin Gal:
Semantics, Modelling, and the Problem of Representation of Meaning - a Brief Survey of Recent Literature. CoRR abs/1402.7265 (2014) - 2013
- [c2]Yarin Gal, Phil Blunsom:
A Systematic Bayesian Treatment of the IBM Alignment Models. HLT-NAACL 2013: 969-977 - 2010
- [c1]Yarin Gal, Mireille Avigal:
Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. ICMLA 2010: 813-818
Coauthor Index
aka: Atilim Gunes Baydin

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