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. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinformatics. 2015;16. 
(851.49 KB)
(851.49 KB). Rapid Serial Visual Presentation for Relevance Feedback in Image Retrieval with EEG Signals. . 2015. (1.38 MB)
(1.38 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). Bags of Local Convolutional Features for Scalable Instance Search. In ACM International Conference on Multimedia Retrieval (ICMR). New York City, NY; USA: ACM; 2016. (3.73 MB)
(3.73 MB). Dublin City University and Partners' Participation in the INS and VTT Tracks at TRECVid 2016. In TRECVID Workshop 2016. Gaithersburg, MD, USA; 2016.
. Shallow and Deep Convolutional Networks for Saliency Prediction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Las Vegas, NV, USA: Computer Vision Foundation / IEEE; 2016. (466.13 KB)
(466.13 KB). Where did I leave my phone ?. Las Vegas, NV, USA: 4th Workshop on Egocentric (First-Person) Vision, CVPR 2016; 2016 . (312.27 KB)
(312.27 KB). Where is my Phone? Personal Object Retrieval from Egocentric Images. In Lifelogging Tools and Applications Workshop in ACM Multimedia. Amsterdam, The Netherlands: ACM; 2016. (1.43 MB)
(1.43 MB). Efficient Combination of Pairwise Feature Networks. In: . Neural Connectomics Challenge. Springer International Publishing; 2017.
. Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback. Long Beach, CA, USA: NIPS 2017 Women in Machine Learning Workshop; 2017. (341.96 KB)
(341.96 KB). Learning Cross-modal Embeddings for Cooking Recipes and Food Images. In CVPR. Honolulu, Hawaii, USA: CVF / IEEE; 2017. (3.37 MB)
(3.37 MB). Magnetic Resonance Imaging as a valuable tool for Alzheimer's disease screening. In Alzheimer’s Association International Conference, London, 2017. 2017.
. Magnetic Resonance Imaging as a valuable tool for Alzheimer's disease screening. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2017;13(7):P1245.
. Object Retrieval with Deep Convolutional Features. In Deep Learning for Image Processing Applications. Amsterdam, The Netherlands: IOS Press; 2017.
. SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. In CVPR 2017 Scene Understanding Workshop (SUNw). Honolulu, Hawaii, USA; 2017. (1.85 MB)
(1.85 MB). SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes. In ICCV Workshop on Egocentric Perception, Interaction and Computing. Venice, Italy: IEEE; 2017. (2.34 MB)
(2.34 MB). The Temporal Dimension of Visual Attention Models. . 2017. (6.98 MB)
(6.98 MB). Towards large scale multimedia indexing: A case study on person discovery in broadcast news. In International Workshop on Content-Based Multimedia Indexing - CBMI 2017. Firenze, Italy; 2017. (831.12 KB)
(831.12 KB). Characteristic Brain Volumetric Changes in the AD Preclinical Signature. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2018;14(7):P1235.
. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. In Alzheimer's Association International Conference. Chicago, USA; 2018.
. Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems. In ACM Multimedia. Seoul, South Korea: ACM; 2018. (11.05 MB)
(11.05 MB). PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks. In ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC). Munich, Germany: Springer; 2018. (3.78 MB)
(3.78 MB). Saliency Weighted Convolutional Features for Instance Search. In Content-Based Multimedia Indexing - CBMI. La Rochelle, France: IEEE; 2018. (3.8 MB)
(3.8 MB). Scanpath and Saliency Prediction on 360 Degree Images. Elsevier Signal Processing: Image Communication. 2018;. (2.61 MB)
(2.61 MB). Assessing Knee OA Severity with CNN attention-based end-to-end architectures. In International Conference on Medical Imaging with Deep Learning (MIDL) 2019. London, United Kingdom: JMLR; 2019. (3.1 MB)
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