| Learning deep features for discriminative localization B Zhou, A Khosla, A Lapedriza, A Oliva, A Torralba Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 4716 | 2016 |
| Learning deep features for scene recognition using places database B Zhou, A Lapedriza, J Xiao, A Torralba, A Oliva Neural Information Processing Systems Foundation, 2014 | 2966 | 2014 |
| Places: A 10 million image database for scene recognition B Zhou, A Lapedriza, A Khosla, A Oliva, A Torralba IEEE transactions on pattern analysis and machine intelligence 40 (6), 1452-1464, 2017 | 1834 | 2017 |
| Object detectors emerge in deep scene cnns B Zhou, A Khosla, A Lapedriza, A Oliva, A Torralba arXiv preprint arXiv:1412.6856, 2014 | 1042 | 2014 |
| Places: An image database for deep scene understanding B Zhou, A Khosla, A Lapedriza, A Torralba, A Oliva arXiv preprint arXiv:1610.02055, 2016 | 299 | 2016 |
| Emotion recognition in context R Kosti, JM Alvarez, A Recasens, A Lapedriza Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 101 | 2017 |
| Way off-policy batch deep reinforcement learning of implicit human preferences in dialog N Jaques, A Ghandeharioun, JH Shen, C Ferguson, A Lapedriza, ... arXiv preprint arXiv:1907.00456, 2019 | 97 | 2019 |
| Understanding the role of individual units in a deep neural network D Bau, JY Zhu, H Strobelt, A Lapedriza, B Zhou, A Torralba Proceedings of the National Academy of Sciences 117 (48), 30071-30078, 2020 | 75 | 2020 |
| Can we do better explanations? A proposal of user-centered explainable AI. M Ribera, A Lapedriza IUI Workshops 2327, 38, 2019 | 73 | 2019 |
| Gender recognition in non controlled environments A Lapedriza, MJ Marín-Jiménez, J Vitria 18th International Conference on Pattern Recognition (ICPR'06) 3, 834-837, 2006 | 67 | 2006 |
| Depth information in human gait analysis: an experimental study on gender recognition R Borràs, À Lapedriza, L Igual International Conference Image Analysis and Recognition, 98-105, 2012 | 47 | 2012 |
| Approximating interactive human evaluation with self-play for open-domain dialog systems A Ghandeharioun, JH Shen, N Jaques, C Ferguson, N Jones, ... arXiv preprint arXiv:1906.09308, 2019 | 45 | 2019 |
| Are all training examples equally valuable? A Lapedriza, H Pirsiavash, Z Bylinskii, A Torralba arXiv preprint arXiv:1311.6510, 2013 | 45 | 2013 |
| Interpreting cnn models for apparent personality trait regression C Ventura, D Masip, A Lapedriza Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 39 | 2017 |
| Context based emotion recognition using emotic dataset R Kosti, JM Alvarez, A Recasens, A Lapedriza IEEE transactions on pattern analysis and machine intelligence 42 (11), 2755 …, 2019 | 34 | 2019 |
| Preferred spatial frequencies for human face processing are associated with optimal class discrimination in the machine MS Keil, A Lapedriza, D Masip, J Vitria PloS one 3 (7), e2590, 2008 | 30 | 2008 |
| Boosted online learning for face recognition D Masip, A Lapedriza, J Vitria IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 …, 2008 | 29 | 2008 |
| Robust gait-based gender classification using depth cameras L Igual, A Lapedriza, R Borras EURASIP Journal on Image and Video Processing 2013 (1), 1-11, 2013 | 27 | 2013 |
| Are external face features useful for automatic face classification? A Lapedriza, D Masip, J Vitria 2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005 | 25 | 2005 |
| Emotion recognition from mid-level features D Sanchez-Mendoza, D Masip, A Lapedriza Pattern Recognition Letters 67, 66-74, 2015 | 21 | 2015 |
