courses:wshop:topics:tematy2024wiosna

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courses:wshop:topics:tematy2024wiosna [2024/03/01 15:34] – [[KKT] YOLO for documents?] kktcourses:wshop:topics:tematy2024wiosna [2024/03/08 12:17] (current) – [[SBK] OpenML dataset creation script for Meta-Learning] admin
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   * **Student:** Przemysław Pawlik   * **Student:** Przemysław Pawlik
-  * **Namespace in the wiki:** FIXME [[..:projects:2024:iiif:]]+  * **Namespace in the wiki:** [[..:projects:2024:iiif:]]
   * **The goal of the project:** The goal of this project is to combine IIIF technology with a knowledge graph to provide metadata-rich descriptions via IIIF stack of technology   * **The goal of the project:** The goal of this project is to combine IIIF technology with a knowledge graph to provide metadata-rich descriptions via IIIF stack of technology
   * **Technology:** Semantic Web, Python/Java   * **Technology:** Semantic Web, Python/Java
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   * **Groups and namespaces in the wiki:**   * **Groups and namespaces in the wiki:**
-    * Maciej Baczyński: FIXME [[..:projects:2024:yolo1:]] +    * Maciej Baczyński: [[..:projects:2024:yolo1:]] 
-    * Aleksandra Jaroszek, Maciej Struski: FIXME [[..:projects:2024:yolo2:]]+    * Aleksandra Jaroszek, Maciej Struski: [[..:projects:2024:yolo2:]]
   * **The goal of the project:** Verify whether state-of-the-art object detection models are usable for documents (manuscripts, printed documents, music scores, etc)   * **The goal of the project:** Verify whether state-of-the-art object detection models are usable for documents (manuscripts, printed documents, music scores, etc)
   * **Technology:** Python, machine learning, data analysis   * **Technology:** Python, machine learning, data analysis
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 ==== [KKT] HWR/HTR state-of-the-art evaluation ==== ==== [KKT] HWR/HTR state-of-the-art evaluation ====
  
-  * **Student:** FIXME +  * **Groups and namespaces in the wiki:** 
-  * **Namespace in the wiki:** [[..:projects:2024:FIXME:]]+    * Kamil Butryn: [[..:projects:2024:hwr1:]] 
 +    * Magdalena Gancarek, Klaudia Korczak: [[..:projects:2024:hwr2:]]
   * **The goal of the project:** Explore and compare state-of-the-art handwriting recognition models/methods and benchmark datasets   * **The goal of the project:** Explore and compare state-of-the-art handwriting recognition models/methods and benchmark datasets
   * **Technology:** Google Scholar/ResearchGate/reading :), Python, machine learning, data analysis   * **Technology:** Google Scholar/ResearchGate/reading :), Python, machine learning, data analysis
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   * **Links:**   * **Links:**
     * [[wp>Handwriting_recognition|Handwriting recognition]]     * [[wp>Handwriting_recognition|Handwriting recognition]]
 +    * [[https://readcoop.eu/transkribus/public-models/|Public models @Transkribus]]
  
 ==== [KKT] Ontology for the SOLARIS synchrotron ==== ==== [KKT] Ontology for the SOLARIS synchrotron ====
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 ==== [KKT] Support in BIRAFFE3 experiment ==== ==== [KKT] Support in BIRAFFE3 experiment ====
  
-  * **Student:** FIXME +  * **Student:** Agnieszka Felis, Mikołaj Golowski 
-  * **Namespace in the wiki:** [[..:projects:2024:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2024:biraffe3:]]
   * **The goal of the project:** Support in BIRAFFE3 experiment   * **The goal of the project:** Support in BIRAFFE3 experiment
   * **Technology:** Python, data analysis   * **Technology:** Python, data analysis
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 ==== [KKT] Emotion prediction in artificial space (then mapped to actual emotion space) ==== ==== [KKT] Emotion prediction in artificial space (then mapped to actual emotion space) ====
  
-  * **Student:** FIXME +  * **Student:** Jan Malczewski 
-  * **Namespace in the wiki:** [[..:projects:2024:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2024:emolatent:]]
   * **The goal of the project:** Create a prediction model that maps features into artificial X-dimensional space. Then, map this space into actual emotions/labels space   * **The goal of the project:** Create a prediction model that maps features into artificial X-dimensional space. Then, map this space into actual emotions/labels space
   * **Technology:** Python, machine learning, data analysis   * **Technology:** Python, machine learning, data analysis
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 ==== [SBK] OpenML dataset creation script for Meta-Learning ==== ==== [SBK] OpenML dataset creation script for Meta-Learning ====
  
-  * **Student:** +  * **Student:** FIXME
   * **Namespace in the wiki:** [[..:projects:2023:openmlds:]]   * **Namespace in the wiki:** [[..:projects:2023:openmlds:]]
   * **The goal of the project:** Prepare a script that will build meta-learnign dataset out of OpenML logs   * **The goal of the project:** Prepare a script that will build meta-learnign dataset out of OpenML logs
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 ==== [SBK] Time-SHAP implementation ==== ==== [SBK] Time-SHAP implementation ====
  
-  * **Student:** FIXME +  * **Student:** Kamil Piskorz 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:windowshap:start]]
   * **The goal of the project:** The goal is to improve the SHAP XAI algorithm to work more efficiently on time-seris data   * **The goal of the project:** The goal is to improve the SHAP XAI algorithm to work more efficiently on time-seris data
   * **Technology:** Python, Keras/PyTorch   * **Technology:** Python, Keras/PyTorch
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 ==== [SBK] Red-Teaming with XAI ==== ==== [SBK] Red-Teaming with XAI ====
-  * **Student:** FIXME +  * **Student:** Jakub Samel 
-  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]+  * **Namespace in the wiki:** [[..:projects:2023:redteam:start:]]
   * **The goal of the project:**    * **The goal of the project:** 
   * **Technology:** Python, Keras/PyTorch   * **Technology:** Python, Keras/PyTorch
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     * Tabular DS: https://www.kaggle.com/datasets/danofer/compass     * Tabular DS: https://www.kaggle.com/datasets/danofer/compass
     * Image DS: take ready-to-use model like and analyze red-team it.     * Image DS: take ready-to-use model like and analyze red-team it.
 +
 +
 +==== [SBK] Dimensionality reduciton to speedup LUX ====
 +  * **Student:** FIXME
 +  * **Namespace in the wiki:** [[..:projects:2023:FIXME:]]
 +  * **The goal of the project:** The goal is to improve LUX software to perform calculation in reduced dimensionality space
 +  * **Technology:** Python, Keras/PyTorch
 +  * **Description:**  LUX (Local Universal Rule-Based Explainer) is an XAI algorithm that produces explanations for any type of machine-learning model. It provides local explanations in a form of human-readable (and executable) rules, but also provide counterfactual explanations as well as visualization of the explanations. It creates explanations by selection of neighborhood data-points which is computationally intensive as it is based on clustering algorithms. In high dims spaces this is inefficient and has limited practical usage due to dimensionality curse. The goal would be to add dimensionality reduction step to the process and test efficiency improvements.
 +  * **Links:**
 +    * https://github.com/sbobek/lux
 +    * https://arxiv.org/abs/2310.14894
 ==== [FIXME] Template ==== ==== [FIXME] Template ====
  
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