escaped '

_publications/2024-01-10-MIDI.md

@@ -26,5 +26,5 @@ One of the challenge would be to find a resonable and interpretable embedding of
 
 <hr/>
 
-Link to original publication with a model: [midi_quantisation_paper_ismir_2022_0.pdf](https://www.turing.ac.uk/sites/default/files/2022-09/midi_quantisation_paper_ismir_2022_0.pdf)
+Link to original publication with a model: [PERFORMANCE MIDI-TO-SCORE CONVERSION BY NEURAL BEAT TRACKING](https://www.turing.ac.uk/sites/default/files/2022-09/midi_quantisation_paper_ismir_2022_0.pdf)
 

_publications/2024-01-12-SAM.md

@@ -2,7 +2,7 @@
 title: "Validation report 003: YOLOv5-license-plate exploration with SHAP"
 collection: publications
 permalink: /publication/2024-01-12-SAM
-excerpt: 'This project investigates the robustness of a fine-tuned YOLOv5 model for license plate detection against DPatch adversarial attacks and explores the interpretability of its predictions through SHAP analysis. The study found that DPatch adversarial attacks reduce the model's detection rates, with discrepancies from previous findings attributed to specific fine-tuning and model advancements. SHAP analysis highlighted the model's focus on specific regions, such as the license plate, providing insights into its decision-making process.'
+excerpt: 'This project investigates the robustness of a fine-tuned YOLOv5 model for license plate detection against DPatch adversarial attacks and explores the interpretability of its predictions through SHAP analysis. The study found that DPatch adversarial attacks reduce the model\'s detection rates, with discrepancies from previous findings attributed to specific fine-tuning and model advancements. SHAP analysis highlighted the model\'s focus on specific regions, such as the license plate, providing insights into its decision-making process.'
 date: 2024-01-12
 venue: 'Explainable Machine Learning 2023/2024 course'
 paperurl: 'https://modeloriented.github.io/CVE-AI/files/2023_SAM.pdf'
@@ -14,15 +14,15 @@ tags:
   - License Plate Detection
 ---
 
-This project investigates the robustness of a YOLOv5-based model(Ultralytics (2021)), finetuned for license plate detection, against adversarial attacks using a variation of DPatch Liu et al. (2019). Additionally, we explore the interpretability of the model’s predictions through SHAP Lundberg and Lee (2017).
+This project investigates the robustness of a YOLOv5-based model (Ultralytics 2021), finetuned for license plate detection, against adversarial attacks using a variation of DPatch Liu et al. (2019). Additionally, we explore the interpretability of the model’s predictions through SHAP (Lundberg and Lee 2017).
 
 <hr/>
 
 This study examines the robustness and interpretability of a fine-tuned YOLOv5 model for license plate detection using DPatch adversarial attacks and SHAP analysis.
 
 DPatch experiments revealed limitations in perturbing the model, with reduced detection rates. Discrepancies from the original DPatch paper were attributed to specific fine-tuning for license plate detection and potential model advancements.
 
-SHAP analysis provided insights into the model’s decision-making, highlighting the local effects of DPatch and the model’s focus on specific regions, such as the license plate. Numbers from Tables 1-3 align with our observations and further solidify our understanding on DPatch attacks.
+SHAP analysis provided insights into the model's decision-making, highlighting the local effects of DPatch and the model's focus on specific regions, such as the license plate. Numbers from Tables 1-3 align with our observations and further solidify our understanding on DPatch attacks.
 
 <hr/>