Towards Combinatorial Interpretability of Neural Computation

Overview

Syllabus

• Introduction to Neural Network Interpretability
Overview of current interpretability techniques
Importance and challenges of interpretability in AI
• Fundamentals of Combinatorial Interpretability
Definition and concepts of combinatorial approaches in interpretation
Historical context and development of combinatorial methods
• The Feature Channel Coding Hypothesis
Introduction to the hypothesis
Theoretical foundation and significance
• Neural Networks and Boolean Expressions
How neural networks represent and compute Boolean functions
Case studies and examples of Boolean computation in neural networks
• Code Interference in Neural Networks
Definition and analysis of code interference
Identifying natural limitations due to interference
• Methods for Mitigating Code Interference
Techniques and strategies to reduce interference effects
Practical applications and case studies
• Experimental Approaches and Tools
Tools and methodologies for combinatorial testing
Designing experiments to evaluate interpretability
• Advanced Techniques in Combinatorial Interpretability
Exploration of cutting-edge research and approaches
Integrating combinatorial methods with other interpretability strategies
• Case Studies and Applications
Real-world applications of combinatorial interpretability
In-depth analysis of successful deployments and outcomes
• Future Directions in Neural Computation Interpretability
Emerging trends and research opportunities
Open questions and potential areas for innovation
• Final Project and Presentations
Guidelines and objectives for the final project
Presentation of findings and peer feedback sessions

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Subjects

Computer Science