Google
Google Scholar
Semantic Scholar
MS Academic
CiteSeerX
ORCIDYarin Gal
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
Atilim Günes Baydin
aka: Atilim Gunes Baydin
aka: Atilim Gunes Baydin
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
load content from web.archive.org
Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
Tweets on dblp homepage
Show tweets from 
on the dblp homepage.
Privacy notice: By enabling the option above, your browser will contact twitter.com and twimg.com to load tweets curated by our Twitter account. At the same time, Twitter will persistently store several cookies with your web browser. While we did signal Twitter to not track our users by setting the "dnt" flag, we do not have any control over how Twitter uses your data. So please proceed with care and consider checking the Twitter privacy policy.
last updated on 2021-03-03 22:25 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
the dblp computer science bibliography is funded by:
