Video

Self-Supervised Learning

Bolei Zhou – From Network Dissection to Policy Dissection: Emergent Concepts in Deep Representations

February 20, 2024

Video

Self-Supervised Learning

Xiao Fu – Multiview and Self-Supervised Representation Learning: Nonlinear Mixture Identification

February 20, 2024

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Self-Supervised Learning

Frederick Klauschen – AI in cancer research and diagnostics – IPAM at UCLA

February 20, 2024

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Self-Supervised Learning

Osbert Bastani – Interpretable Machine Learning via Program Synthesis – IPAM at UCLA

February 20, 2024

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Self-Supervised Learning

Klaus Robert Muller – AI for the Sciences: Recent Developments for XAI – IPAM at UCLA

February 20, 2024

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Self-Supervised Learning

Jaesik Choi – Explainable AI to Analyze Internal Decision Mechanism of Deep Neural Networks

February 20, 2024

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Self-Supervised Learning

Bin Yu – Interpreting Deep Neural Networks towards Trustworthiness – IPAM at UCLA

February 20, 2024

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Self-Supervised Learning

Evrim Acar – Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

February 20, 2024

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Self-Supervised Learning

Wojciech Samek – Concept-Level Explainable AI – IPAM at UCLA

February 20, 2024

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Self-Supervised Learning

Cecelia Clementi – Designing molecular models with machine learning and experimental data

February 20, 2024

Category - AI Science

Computer Vision Deep Learning Markov Models Neural Network Quantum Computing in AI Reinforcement Learning Self-Supervised Learning Support Vector Machines (SVM)Transformer Architecture

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

23. Model Merging, Cross-Modal Coupling, Course Summary

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

22. Probabilistic Inference II

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

21. Probabilistic Inference I

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

19. Architectures: GPS, SOAR, Subsumption, Society of Mind

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

18. Representations: Classes, Trajectories, Transitions

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

17. Learning: Boosting

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

15. Learning: Near Misses, Felicity Conditions

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

14. Learning: Sparse Spaces, Phonology

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

13. Learning: Genetic Algorithms

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

12. Learning: Neural Nets, Back Propagation

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

11. Learning: Identification Trees, Disorder

MACHINE LEARNING COURSE - MIT, Support Vector Machines (SVM)

10. Introduction to Learning, Nearest Neighbors