IDL RESEARCH SUMMER SCHOOL 2024
The course is available for PhD fellows with scholarship and industry professionals.
*Application deadline: April15th, 2024
The course is limited to 25 places.
Interpretability in
Deep Learning
Welcome to the summer school “Interpretability in Deep Learning 2024”, organized by the DLN center at UiT The Arctic University of Norway!
Oncampus Summer School: 7th - 9th August, 2024
Venue : TEKNOBYGGET 1,022AUD, Tromsø, Norway
Application Link: https://uit.no/utdanning/emner/emne/842308/inf-8605
Dedicated Course Page: https://www.indeeplearning.org/course
Artificial intelligence (AI) and machine learning approaches are often considered as black boxes, i.e. as a type of algorithms that accomplish learning tasks but cannot explain their knowledge. However, as AI is increasingly adopted for accomplishing human cognitive tasks, it is becoming important that the AI models are understandable. In critical tasks such as deriving, from given data, a correct medical diagnosis and prognosis, collaboration between artificial and human intelligence in imperative so that the suggestions or decision from AI are both more accurate and more trustworthy. ​This intensive course will consider different topics of importance regarding explainable AI, equipping the students with knowledge of approaches that can be used to interpret AI, and AI approaches that are more explainable than others. In addition, the students will receive practical skills of applying selected approaches, which will equip the students with practical skills of adapting to the rapid pace of technology development in the field of explainable AI.
Introductory Concept
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Unveiling the Black-box Problem
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Tracing the Evolution of Interpretability and Explainability
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Knowledge versus Performance, Need of Explainability
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Overview of Course & Learning Resources
3 hours
Explainable Approaches
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Understanding Knowledge Encoding, Feature Importance and Feature Interactions
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Neural Network Perturbation: Role of Neurons, LIME, Occlusion
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Theory and Intuition behind CAM
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Neural Network Gradient: CAM, Grad-CAM, Saliency, LRP.
6 hours
Model-agnostic Techniques
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Abstract Encoding Concept
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Case Study: Visual Propagation-based Explanations
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Sanity Check and Evaluation Criteria – Randomization Test
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Introduction to Adversarial Attacks and Defenses -
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Counterfactual Reasoning and Causal Inference Models
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Bias Estimation and Mitigation
5 hours
Neural Network and Explainability
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Segregating methods into the new class of representation
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Harnessing the Power of Self-interpretable Models: Towards Robust Interpretability
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Interpretability Direction Towards Sustainable AI
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Understanding the nature of bio-medical microscopy application
5 hours
Ethics & Emerging Trend in AI
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Ethics in Artificial Intelligence
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Diverse scenarios and impact on industrial standards
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Fairness, accountability, and transparency in deep learning
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Review of GDPR regulation
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Emerging trends in explainable AI
1 hours
NOTE: First introductory lecture was held on 28th May 2024 (online). [YouTube]
Part of the lectures will be uploaded as video lectures, which is a prerequisite before the onsite (at UiT) program begins in August. The details have been shared during the first digital introductory.
