| Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments C Ionescu, D Papava, V Olaru, C Sminchisescu Pattern Analysis and Machine Intelligence, IEEE Transactions on 36 (7), 1325 …, 2014 | 3721 | 2014 |
| Perceiver io: A general architecture for structured inputs & outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... arXiv preprint arXiv:2107.14795, 2021 | 553 | 2021 |
| Matrix backpropagation for deep networks with structured layers C Ionescu, O Vantzos, C Sminchisescu Proceedings of the IEEE international conference on computer vision, 2965-2973, 2015 | 330 | 2015 |
| Latent Structured Models for Human Pose Estimation C Ionescu, F Li, C Sminchisescu | 327* | |
| Using fast weights to attend to the recent past J Ba, GE Hinton, V Mnih, JZ Leibo, C Ionescu Advances in neural information processing systems 29, 2016 | 275 | 2016 |
| Unsupervised learning of object keypoints for perception and control TD Kulkarni, A Gupta, C Ionescu, S Borgeaud, M Reynolds, A Zisserman, ... Advances in neural information processing systems 32, 2019 | 214 | 2019 |
| Unsupervised control through non-parametric discriminative rewards D Warde-Farley, T Van de Wiele, T Kulkarni, C Ionescu, S Hansen, ... arXiv preprint arXiv:1811.11359, 2018 | 185 | 2018 |
| Iterated second-order label sensitive pooling for 3d human pose estimation C Ionescu, J Carreira, C Sminchisescu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 108 | 2014 |
| Training deep networks with structured layers by matrix backpropagation C Ionescu, O Vantzos, C Sminchisescu arXiv preprint arXiv:1509.07838, 2015 | 102 | 2015 |
| Random Fourier approximations for skewed multiplicative histogram kernels F Li, C Ionescu, C Sminchisescu Pattern Recognition, 262-271, 2010 | 90 | 2010 |
| Structural SVM for visual localization and continuous state estimation C Ionescu, L Bo, C Sminchisescu Computer Vision, 2009 IEEE 12th International Conference on, 1157-1164, 2009 | 51 | 2009 |
| Making sense of reinforcement learning and probabilistic inference B O'Donoghue, I Osband, C Ionescu arXiv preprint arXiv:2001.00805, 2020 | 47 | 2020 |
| Large-scale data-dependent kernel approximation C Ionescu, A Popa, C Sminchisescu Artificial Intelligence and Statistics, 19-27, 2017 | 12 | 2017 |
| Human3. 6M C Ionescu, D Papava, V Olaru, C Sminchisescu Ieee Transactions on Pattern Analysis and Machine intelligence, 1, 2014 | 11 | 2014 |
| Reinforcement learning neural networks grounded in learned visual entities CD Ionescu, TD Kulkarni US Patent 10,748,039, 2020 | 10 | 2020 |
| Feature-based pose estimation C Sminchisescu, L Bo, C Ionescu, A Kanaujia Visual Analysis of Humans: Looking at People, 225-251, 2011 | 9 | 2011 |
| HiP: Hierarchical Perceiver J Carreira, S Koppula, D Zoran, A Recasens, C Ionescu, O Henaff, ... arXiv preprint arXiv:2202.10890, 2022 | 4 | 2022 |
| Learning from One Continuous Video Stream J Carreira, M King, V Patraucean, D Gokay, C Ionescu, Y Yang, D Zoran, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Generating neural network outputs by cross attention of query embeddings over a set of latent embeddings AC Jaegle, JB Alayrac, SBD Avocat, CD Ionescu, C Doersch, F Ding, ... US Patent App. 18/284,595, 2024 | | 2024 |
| Predicting protein amino acid sequences using generative models conditioned on protein structure embeddings AW Senior, S Kohl, J Yim, RJ Bates, CD Ionescu, CTC Nash, ... US Patent App. 18/275,933, 2024 | | 2024 |
