In this thread all special talks in our CogSys colloq will be announced
MA Verteidigung Daniel Gramelt: Explanatory interactive machine learning for welding seam quality assessment (MA AI, in cooperation with Porsche digital)
Zeit: 15.04.2024 - 12:00 - 13:00
Ort: WE5/05.013 und Teams
Information to participate via Teams:
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Besprechungs-ID: 384 493 620 804
Kennung: qwfKMu
Zeit: 15.04.2024 - 12:00 - 13:00
Ort: WE5/05.013 und Teams
Information to participate via Teams:
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Besprechungs-ID: 384 493 620 804
Kennung: qwfKMu
Talk by David Cerna: Anti-unification: Introduction, Applications, and Recent Results
Time: 11.04.2024 - 17:00-18:00
Where: WE5/05.013 and Teams
Anti-unification is a method for symbolically generalizing formal expression. It was
introduced independently by Plotkin and Reynolds as an operation for inductive infer-
encing. Though conceptually simple, it is an effective tool for abstraction and templat-
ing. Since the seminal work, the number of applications has grown tremendously with
uses in program analysis, program repair, library compression, automated reasoning, and
beyond. With the growth of applications, there has been an effort to strengthen the the-
oretical foundations of the subject. In this talk, we introduce anti-unification, overview
the existing applications, and discuss recent theoretical results concerning equational and
high-order anti-unification.
TEAMS:
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Besprechungs-ID: 361 014 049 591
Kennung: 8TBetS
Time: 11.04.2024 - 17:00-18:00
Where: WE5/05.013 and Teams
Anti-unification is a method for symbolically generalizing formal expression. It was
introduced independently by Plotkin and Reynolds as an operation for inductive infer-
encing. Though conceptually simple, it is an effective tool for abstraction and templat-
ing. Since the seminal work, the number of applications has grown tremendously with
uses in program analysis, program repair, library compression, automated reasoning, and
beyond. With the growth of applications, there has been an effort to strengthen the the-
oretical foundations of the subject. In this talk, we introduce anti-unification, overview
the existing applications, and discuss recent theoretical results concerning equational and
high-order anti-unification.
TEAMS:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_YWEwY2ZiNzYtOWIzMi00N2YwLTgwZTUtYmZiZTVlY2UzMDI4%40thread.v2/0?context=%7b%22Tid%22%3a%224f18ddfc-c31f-4597-afda-fa5a760bf3cf%22%2c%22Oid%22%3a%224d7684e9-7cb5-4b10-b8b3-cae1a82ac504%22%7d
Besprechungs-ID: 361 014 049 591
Kennung: 8TBetS
Presentation Slides:
Vortrag von:
Annabel Lindner (FAU), Doktorandin in der Informatik-Didaktik
Titel:
How to Deal with Transformative Topics in Computer Science Education: An Analysis Based on the Topic of Artificial Intelligence
Annabel Lindner (FAU), Doktorandin in der Informatik-Didaktik
Titel:
How to Deal with Transformative Topics in Computer Science Education: An Analysis Based on the Topic of Artificial Intelligence
Zeit: 19.06.2024 - 16:00 - 17:30
Ort: WE5/05.013 und Teams
Information to participate via Teams:
Ort: WE5/05.013 und Teams
Information to participate via Teams:
Meeting ID: 354 320 751 078
Passcode: CmdZ5p
Vortrag von:
Lukas Gernlein
Titel:
Towards Explainable and Interactive Machine Learning for Multi-Label Classification
Lukas Gernlein
Titel:
Towards Explainable and Interactive Machine Learning for Multi-Label Classification
Abstract:
The opaque nature of neural networks hinders applicants from fully retracing the prediction done by deep learning classifiers. When employing neural networks in critical applications such as medical diagnosis or autonomous driving, predictions relying on incorrect decision boundaries can have fatal effects. Therefore, methods are needed to explain the prediction process of deep learning classifiers and to interact with these explanations to prevent malicious behaviour. Our work provides a method to interact with classifiers explanations and enable feedback through a novel loss function, producing robust and reliable models for complex multi-label tasks. We introduce the multi-label right for the right reason loss function (MuLRRR), which, besides right answers, ensures correct reasoning by using label-specific annotation masks on integrated gradient explanations corresponding to a classifier’s prediction, where outlying gradients are penalized. Our contribution is combining and extending two approaches in the XIML research. We use integrated gradients to explain a classifier’s prediction, which is constrained over the extended right for the right reason loss function. Additionally, we derive a metric to evaluate a model’s multi-label explanation performance. This multi-label explanation score (MuLX) uses integrated gradients for explanations and is a ratio of correctly highlighted parts of the input to incorrect activated locations, which indicates how well a model focuses on correct parts for its forecasts. The experiments showed that our MuLRRR loss function enhanced classification metrics and the proposed MuLX score compared to common binary cross entropy constraints.
Zeit: 29.05.2024 - 16:00 - 17:30
Ort: WE5/05.013 und Teams
Information to participate via Teams:
Ort: WE5/05.013 und Teams
Information to participate via Teams:
Meeting ID: 354 320 751 078
Passcode: CmdZ5p