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] . The segmentation of images via scale-space trees. In British Machine Vision Conference. Southampton, UK; 1998. pp. 33–43. 
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(1.18 MB). New interaction modes for rich panoramic live video experiences. Behaviour & Information Technology. 2014;33(8).
. Machine Learning for Genomic Sequence Processing. . 2022 .
. Adversarial Learning for Feature Shift Detection and Correction. In Neural Information Processing Systems (NeurIPS). New Orleans, USA; 2023.
. Predicting Dog Phenotypes from Genotypes. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'22). 2022. (1.17 MB)
(1.17 MB). Fast and Robust Edge Extraction in Unorganized Point Clouds. In International Conference on Digital Image Computing: Techniques and Applications. Adelaide, Australia: DICTA/IEEE; 2015. (700.25 KB)
(700.25 KB). Segmentation-based Multi-Scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds. In International Conference on Computer Vision Theory and Applications. Porto, Portugal; 2017. (4.3 MB)
(4.3 MB). Study of Normalization and Aggregation Approaches for Consensus Network Estimation. In 2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Artificial Life (2015 IEEE ALIFE). Cape Town, South Africa; 2015. (317 KB)
(317 KB). Efficient combination of pairwise feature networks. JMLR: Workshop and Conference Proceedings . Nancy, France: JMLR: Workshop and Conference Proceedings; 2014;46:77 - 84. (217.39 KB)
(217.39 KB). Unsupervised GRN Ensemble. In Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology . New York, NY: Springer science, Humana Press; 2019. pp. 283-302.
. Study of Gene Regulatory Networks Inference Methods from Gene Expression Data. . 2017. (2.73 MB)
(2.73 MB). Efficient Combination of Pairwise Feature Networks. In: . Neural Connectomics Challenge. Springer International Publishing; 2017.
. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinformatics. 2015;16. (851.49 KB)
(851.49 KB). Efficient search of objects in images using deep reinforcement learning. NIPS Women in Machine Learning Workshop. Barcelona.; 2016.
. Budget-aware Semi-Supervised Semantic and Instance Segmentation. In CVPR 2019 DeepVision Workshop. Long Beach, CA, USA: OpenCVF; 2019. (6.59 MB)
(6.59 MB). Efficient Exploration of Region Hierarchies for Semantic Segmentation. . 2015. (11.62 MB)
(11.62 MB). Detection-aided medical image segmentation using deep learning. . 2017. (7.07 MB)
(7.07 MB). Detection-aided liver lesion segmentation using deep learning. In ML4H: Machine Learning for Health Workshop at NIPS 2017. 2017. (1 MB)
(1 MB). Image and Video Object Segmentation in Low Supervision Scenarios. . Computer Architectures. [Barcelona]: Universitat Politecnica de Catalunya; 2021.
. Hierarchical Object Detection with Deep Reinforcement Learning. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.
. Mask-guided sample selection for Semi-Supervised Instance Segmentation. Multimedia Tools and Applications. 2020;. (2.2 MB)
(2.2 MB). Hierarchical Object Detection with Deep Reinforcement Learning. In Deep Reinforcement Learning Workshop, NIPS 2016. 2016. (877.51 KB)
(877.51 KB). RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation. Multimedia Tools and Applications. 2022;. (5.78 MB)
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