Media Summary: Talk at Applied Category Theory (ACT) 2024 University of Oxford, Department of Computer Science Speaker: All attendees have received an email regarding access to the QWoF Slack workspace. If you have not accepted the invitation, click ... Talk at Applied Category Theory 2023 Full Title: Active Inference in String Diagrams: A Categorical Account of Predictive ...

Sean Tull Towards Compositional Interpretability - Detailed Analysis & Overview

Talk at Applied Category Theory (ACT) 2024 University of Oxford, Department of Computer Science Speaker: All attendees have received an email regarding access to the QWoF Slack workspace. If you have not accepted the invitation, click ... Talk at Applied Category Theory 2023 Full Title: Active Inference in String Diagrams: A Categorical Account of Predictive ... Plenary Talk at Applied Category Theory 2023 This is a summary of findings from recent work, available as a preprint linked in Ref ... Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ... Stanford AI Lab Faculty Lunch, November 7, 2025. Updated version of 0:59 ...

Dhanya Sridhar (IVADO + Université de Montréal + Mila) ... Scholars working at the interface of statistics, machine learning, and finance will review statistical and machine learning ideas and ... This week, we're discussing "Decomposing Language Models Into Understandable Components", which addresses the challenge ...

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Sean Tull - Towards Compositional Interpretability for XAI
Sean Tull - Towards Compositional Interpretability for XAI - IPAM at UCLA
(QNLP20) Sean Tull: Compositional Semantics with Formal Concept Analysis
Sean Tull - Active Inference in String Diagrams
Sean Tull - Generalised integrated information theories
ActInf MathStream #006.1 ~ Sean Tull "Active Inference in String Diagrams"
Sean Tull - Causal models in string diagrams
Sean Tull: Integrated Information in Process Theories
The Dark Matter of AI [Mechanistic Interpretability]
Assessing skeptical views of interpretability research
Causal Representation Learning: A Natural Fit for Mechanistic Interpretability
Explainable AI by Design via Semantic Information Pursuit (René Vidal)
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Sean Tull - Towards Compositional Interpretability for XAI

Sean Tull - Towards Compositional Interpretability for XAI

Talk at Applied Category Theory (ACT) 2024 University of Oxford, Department of Computer Science Speaker:

Sean Tull - Towards Compositional Interpretability for XAI - IPAM at UCLA

Sean Tull - Towards Compositional Interpretability for XAI - IPAM at UCLA

Recorded 06 November 2024.

(QNLP20) Sean Tull: Compositional Semantics with Formal Concept Analysis

(QNLP20) Sean Tull: Compositional Semantics with Formal Concept Analysis

All attendees have received an email regarding access to the QWoF Slack workspace. If you have not accepted the invitation, click ...

Sean Tull - Active Inference in String Diagrams

Sean Tull - Active Inference in String Diagrams

Talk at Applied Category Theory 2023 Full Title: Active Inference in String Diagrams: A Categorical Account of Predictive ...

Sean Tull - Generalised integrated information theories

Sean Tull - Generalised integrated information theories

Sean Tull

ActInf MathStream #006.1 ~ Sean Tull "Active Inference in String Diagrams"

ActInf MathStream #006.1 ~ Sean Tull "Active Inference in String Diagrams"

"Active Inference in String Diagrams"

Sean Tull - Causal models in string diagrams

Sean Tull - Causal models in string diagrams

Plenary Talk at Applied Category Theory 2023 This is a summary of findings from recent work, available as a preprint linked in Ref ...

Sean Tull: Integrated Information in Process Theories

Sean Tull: Integrated Information in Process Theories

Sean Tull

The Dark Matter of AI [Mechanistic Interpretability]

The Dark Matter of AI [Mechanistic Interpretability]

Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ...

Assessing skeptical views of interpretability research

Assessing skeptical views of interpretability research

Stanford AI Lab Faculty Lunch, November 7, 2025. Updated version of https://web.stanford.edu/~cgpotts/blog/interp/ 0:59 ...

Causal Representation Learning: A Natural Fit for Mechanistic Interpretability

Causal Representation Learning: A Natural Fit for Mechanistic Interpretability

Dhanya Sridhar (IVADO + Université de Montréal + Mila) ...

Explainable AI by Design via Semantic Information Pursuit (René Vidal)

Explainable AI by Design via Semantic Information Pursuit (René Vidal)

Scholars working at the interface of statistics, machine learning, and finance will review statistical and machine learning ideas and ...

Towards Monosemanticity: Decomposing Language Models Into Understandable Components

Towards Monosemanticity: Decomposing Language Models Into Understandable Components

This week, we're discussing "Decomposing Language Models Into Understandable Components", which addresses the challenge ...