| Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures J Bergstra, D Yamins, D Cox International conference on machine learning, 115-123, 2013 | 3222 | 2013 |
| fMRI "Brain Reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex DD Cox, RL Savoy Neuroimage 19 (2), 261-270, 2003 | 1374* | 2003 |
| Visual place recognition: A survey S Lowry, N Sünderhauf, P Newman, JJ Leonard, D Cox, P Corke, ... ieee transactions on robotics 32 (1), 1-19, 2015 | 1256 | 2015 |
| Deep predictive coding networks for video prediction and unsupervised learning W Lotter, G Kreiman, D Cox International Conference of Learning Representations (ICLR), 2017 | 1204 | 2017 |
| Untangling invariant object recognition JJ DiCarlo, DD Cox Trends in cognitive sciences 11 (8), 333-341, 2007 | 1151 | 2007 |
| Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. J Bergstra, D Yamins, DD Cox SciPy 13, 20, 2013 | 1046 | 2013 |
| Hyperopt: a python library for model selection and hyperparameter optimization J Bergstra, B Komer, C Eliasmith, D Yamins, DD Cox Computational Science & Discovery 8 (1), 014008, 2015 | 993 | 2015 |
| Why is real-world visual object recognition hard? N Pinto, DD Cox, JJ DiCarlo PLoS computational biology 4 (1), e27, 2008 | 772 | 2008 |
| On the information bottleneck theory of deep learning AM Saxe, Y Bansal, J Dapello, M Advani, A Kolchinsky, BD Tracey, ... Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124020, 2019 | 691 | 2019 |
| Large-scale optimization of hierarchical features for saliency prediction in natural images E Vig, M Dorr, D Cox Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 531 | 2014 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... JAMA network open 3 (3), e200265-e200265, 2020 | 400 | 2020 |
| A high-throughput screening approach to discovering good forms of biologically inspired visual representation N Pinto, D Doukhan, JJ DiCarlo, DD Cox PLoS computational biology 5 (11), e1000579, 2009 | 396 | 2009 |
| Chemosensory cues to conspecific emotional stress activate amygdala in humans LR Mujica-Parodi, HH Strey, B Frederick, R Savoy, D Cox, Y Botanov, ... PloS one 4 (7), e6415, 2009 | 343 | 2009 |
| Threedworld: A platform for interactive multi-modal physical simulation C Gan, J Schwartz, S Alter, D Mrowca, M Schrimpf, J Traer, J De Freitas, ... arXiv preprint arXiv:2007.04954, 2020 | 321 | 2020 |
| Principle-driven self-alignment of language models from scratch with minimal human supervision Z Sun, Y Shen, Q Zhou, H Zhang, Z Chen, D Cox, Y Yang, C Gan Advances in Neural Information Processing Systems 36, 2511-2565, 2023 | 316 | 2023 |
| Beyond simple features: A large-scale feature search approach to unconstrained face recognition D Cox, N Pinto 2011 IEEE International Conference on Automatic Face & Gesture Recognition …, 2011 | 282 | 2011 |
| Triton: an intermediate language and compiler for tiled neural network computations P Tillet, HT Kung, D Cox Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine …, 2019 | 270 | 2019 |
| How far can you get with a modern face recognition test set using only simple features? N Pinto, JJ DiCarlo, DD Cox 2009 IEEE conference on computer vision and pattern recognition, 2591-2598, 2009 | 266 | 2009 |
| Neural networks and neuroscience-inspired computer vision DD Cox, T Dean Current Biology 24 (18), R921-R929, 2014 | 261 | 2014 |
| Recurrent computations for visual pattern completion H Tang, M Schrimpf, W Lotter, C Moerman, A Paredes, J Ortega Caro, ... Proceedings of the National Academy of Sciences 115 (35), 8835-8840, 2018 | 250 | 2018 |
James BergstraPrincipal Engineer, Ocado Technologyocado.com üzerinde doğrulanmış e-posta adresine sahip