In my GitHub, you will find the source code used in my publications (listed below) that include strategies for compression and acceleration of deep networks, and compact representations for large datasets. Alternatively, you can check these projects and publications on my homepage.
- Ian Pons, Bruno Yamamoto, Anna H. Reali Costa, Artur Jordão: Effective Layer Pruning Through Similarity Metric Perspective. In: International Conference on Machine Learning (ICML), 2024, Viena, Áustria.
- Artur Jordão, George Araújo, Helena de Almeida Maia and Hélio Pedrini: When Layers Play the Lottery, all Tickets Win at Initialization. In: International Conference on Computer Vision (ICCV), 2023, Paris, France.
- Artur Jordão and Hélio Pedrini: On the Effect of Pruning on Adversarial Robustness. In: International Conference on Computer Vision (ICCV), 2021, Virtual. Poster.
- Artur Jordão, Maiko Lie, Victor Hugo Cunha de Melo, William Robson Schwartz: Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method. In: Winter Conference on Applications of Computer Vision (WACV), 2021, Hawaii. GitHub
- Artur Jordão, Maiko Lie, Fernando Yamada, William Robson Schwartz: Stage-Wise Neural Architecture Search. In: International Conference on Pattern Recognition (ICPR), 2020, Milan, Italy. GitHub
- Artur Jordão, Fernando Yamada, William Robson Schwartz: Deep Network Compression based on Partial Least Squares In: Neurocomputing, 2020. GitHub
- Artur Jordão, Maiko Lie, William Robson Schwartz: Discriminative Layer Pruning for Convolutional Neural Networks In: IEEE Journal of Selected Topics In Signal Processing, 2020. GitHub
Word cloud illustrating the content of my papers.
