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. Multi-camera multi-object voxel-based Monte Carlo 3D tracking strategies. Eurasip journal on advances in signal processing. 2011;2011:1–15.
. Towards a bayesian approach to robust finding correspondences in multiple view geometry environments. In Workshop on Computer Graphics and Geometric Modelling. Intrernational Conference on Computational Science. 2005. pp. 281–289. 
(823.11 KB)
(823.11 KB). Towards a Bayesian Approach to Robust Finding Correspondances in Multiple View Geometry Environments. In Computational Science – ICCS 2005. Berlin / Heidelberg: Springer; 2005. pp. 281–289.
. Importance Weighted Evolution Strategies. In NeurIPS 2018 Deep Reinforcement Learning Workshop . Montreal, Quebec; 2018. (362.25 KB)
(362.25 KB). From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction. Image and Vision Computing. 2017;. (1.92 MB)
(1.92 MB). Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills. In International Conference on Machine Learning (ICML) 2020. 2020. (6.89 MB)
(6.89 MB). Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks. In International Conference on Learning Representations (ICLR). 2018.
. Layer-wise CNN Surgery for Visual Sentiment Prediction. . 2015. (1.51 MB)
(1.51 MB). Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks. In NIPS Time Series Workshop 2017. Long Beach, CA, USA; 2017. (427.72 KB)
(427.72 KB). Deep Learning that Scales: Leveraging Compute and Data. . Computer Architecture. [Barcelona, Catalonia]: Universitat Politècnica de Catalunya; 2020. (8.55 MB)
(8.55 MB). Learning to Skip State Updates in Recurrent Neural Networks. . 2017. (961.49 KB)
(961.49 KB). Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster. In International Conference on Computational Science (ICCS). Zurich, Switzerland: Elsevier; 2017. (576.67 KB)
(576.67 KB). Sentiment concept embedding for visual affect recognition. In Multimodal Behavior Analysis in theWild. 1st ed. Elsevier; 2018.
. Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction. In 1st International Workshop on Affect and Sentiment in Multimedia. Brisbane, Australia: ACM; 2015. (506.22 KB)
(506.22 KB). SLAM-based 3D outdoor reconstructions from LIDAR data. In IC3D. Brussels, Belgium: IEEE; 2018. (3.21 MB)
(3.21 MB). Identifying brain ageing trajectories using variational autoencoders with regression model in neuroimaging data stratified by sex and validated against dementia-related risk factors. In 7th International Workshop on PRedictive Intelligence in MEdicine, MICCAI 2024. 2024.
. Identifying brain ageing trajectories using variational autoencoders. In PRedictive Intelligence in MEdicine. Springer International Publishing; 2025.
. Region merging parameter dependency as information diversity to create sparse hierarchies of partitions. In 2010 IEEE International Conference on Image Processing. 2010. pp. 2237–2240.
. Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation Results. . Universitat Politècnica de Catalunya (UPC); 2010. (57.57 MB)
(57.57 MB). Hierarchical segmentation of vegetation areas in high spatial resolution images by fusion of multispectral information. In 2009 IEEE International Geoscience and Remote Sensing Symposium. 2009. pp. 232–235.
. General region merging approaches based on information theory statistical measures. In IEEE International Conference on Image Processing. 2008. pp. 3016–3019.
. General Region Merging Based on First Order Markov Information Theory Statistical Measures. In 16th European Signal Processing Conference. 2008.
. Multiple view region matching using as a Lagrangian optimization problem. In 2007 International Conference on Acoustics, Speech and Signal Processing. 2007.
. Image Analysis and Understanding Based on Information Theoretical Region Merging Approaches for Segmentation and Cooperative Fusion. In Handbook of Research on Computational Intelligence for Engineering, Science, and Business. IGI Global; 2012. pp. 75-121.