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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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publications
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Published in 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of this paper is to propose a unique approach that could be applied on very different paradigms. To demonstrate the robustness of this approach, we conducted a meta-analysis on multiple datasets for three BCI paradigms: event-related potentials (P300), motor imagery and SSVEP. Relying on the MOABB open source framework to ensure the reproducibility of the experiments and the statistical analysis, the results clearly show that the proposed approach could be applied on any kind of BCI paradigm and in most of the cases to significantly improve the classifier reliability. We point out some key features to further improve transfer learning methods.
Recommended citation: S. Khazem, S. Chevallier, Q. Barthélemy, K. Haroun and C. Noûs, "Minimizing Subject-dependent Calibration for BCI with Riemannian Transfer Learning," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021, pp. 523-526, doi: 10.1109/NER49283.2021.9441279 https://arxiv.org/pdf/2111.12071.pdf
Deep learning for the detection of semantic features in tree X-ray CT scans
Published in Journal: Artificial Intelligence in Agriculture, 2023
According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.
Recommended citation: KHAZEM, Salim, RICHARD, Antoine, FIX, Jeremy, et al. Deep learning for the detection of semantic features in tree X-ray CT scans. Artificial Intelligence in Agriculture, 2023, vol. 7, p. 13-26, doi: 10.1016/j.aiia.2022.12.001 https://www.sciencedirect.com/science/article/pii/S2589721722000289
Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation
Published in International Conference on Computer Vision Systems, 2023
The quality of a wood log in the wood industry depends heavily on the presence of both outer and inner defects, including inner knots that are a result of the growth of tree branches. Today, locating the inner knots require the use of expensive equipment such as X-ray scanners. In this paper, we address the task of predicting the location of inner defects from the outer shape of the logs. The dataset is built by extracting both the contours and the knots with X-ray measurements. We propose to solve this binary segmentation task by leveraging convolutional recurrent neural networks. Once the neural network is trained, inference can be performed from the outer shape measured with cheap devices such as laser profilers. We demonstrate the effectiveness of our approach on fir and spruce tree species and perform ablation on the recurrence to demonstrate its importance
Recommended citation: KHAZEM, Salim, FIX, Jeremy, et PRADALIER, Cedric. Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation. arXiv preprint arXiv:2308.11291, 2023. https://arxiv.org/pdf/2308.11291.pdf
teaching
Machine learning
Graduate course, CentraleSupelec, SDI, 2022
- Ensemble Methods
- Decision trees
- Bagging
- Boosting
- Support vector machines (SVM)
- Kernels
- Classification
- Regression
- Vector Quantization
- K-means
- Incremental neural networks
- Self-organizing Maps
Natural Language Processing
Graduate course, CentraleSupelec, SDI, 2023
- Language Modeling
- Sequence labeling tasks
- General NLP tasks:
- Sentiment analysis
- Classfication
- NER