added abstracts

_publications/2024-01-10-MIDI.md

@@ -2,7 +2,7 @@
 title: "Validation report 001: MIDI-to-score Conversion Model"
 collection: publications
 permalink: /publication/2024-01-10-MIDI
-excerpt: 'This paper is about fixing template issue #693.'
+excerpt: 'This project aims to explore advancements in automatically transcribing performance MIDI streams into musical scores, focusing on the paper "Performance MIDI-to-score Conversion by Neural Beat Tracking" by Liu et al. (2022). The study reveals artifacts in score generation models, particularly with velocity contributions and time signature robustness, and highlights the need for more tailored explainable AI (XAI) methods for symbolic music data to enhance model interpretability.'
 date: 2024-01-10
 venue: 'Explainable Machine Learning 2023/2024 course'
 paperurl: 'https://modeloriented.github.io/CVE-AI/files/2023_MIDI.pdf'
@@ -21,7 +21,8 @@ We investigate the model's behavior using sequence perturbations and a LIME-like
 We were able to discover certain artifacts of the score generation models, especially when it comes to velocity contribution to hand part assignment. The time signature model is not fully robust to alterations that should not have affect the output.
 
 Unfortunately, the proposed methods have drawbacks and are not fully justified. Current XAI methods do not work well with symbolic music data in general. Developing
-more tailored and adaptable XAI methods for musical applications could contribute to improved model interpretability. One of the challenge would be to find a re
+more tailored and adaptable XAI methods for musical applications could contribute to improved model interpretability. 
+One of the challenge would be to find a resonable and interpretable embedding of the pitch space that encodes musical features.
 
 <hr/>
 

_publications/2024-01-11-GO.md

@@ -2,7 +2,7 @@
 title: "Validation report 002: Go Policy Networks"
 collection: publications
 permalink: /publication/2024-01-11-GO
-excerpt: 'This paper is about fixing template issue #693.'
+excerpt: 'This report identifies shortcomings of using convolutional architectures as Go policy networks by comparing them to Transformer policies. The findings show that while convolutional networks excel at capturing local features, they struggle with global phenomena, which can be detrimental in games like Go. Transformers, with their flexible attention mechanisms, better incorporate both local and global understanding, suggesting potential for future research in using Transformers for large positional games.'
 date: 2024-01-11
 venue: 'Explainable Machine Learning 2023/2024 course'
 paperurl: 'https://modeloriented.github.io/CVE-AI/files/2023_GO.pdf'