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Self-Supervised Learning

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

February 20, 2024

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Self-Supervised Learning

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

February 20, 2024

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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

Wojciech Samek – Concept-Level Explainable AI – 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

Bin Yu – Interpreting Deep Neural Networks towards Trustworthiness – 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

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

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

Category - Self-Supervised Learning

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 I Societal impact of ML (Guest lecture by Prof. James Zou) I 2022 I Lecture 18

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 I Basic concepts in RL, Value iteration, Policy iteration I 2022 I Lecture 17

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Self-supervised learning I 2022 I Lecture 16

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I PCA/ICA I 2022 I Lecture 15

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Factor Analysis/PCA I 2022 I Lecture 14

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I GMM (EM) I 2022 I Lecture 13

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 I K-Means, GMM (non EM), Expectation Maximization I 2022 I Lecture 12

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Feature / Model selection, ML Advice I 2022 I Lecture 11

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Bias – Variance, Regularization I 2022 I Lecture 10

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Neural Networks 2 (backprop) I 2022 I Lecture 9

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Neural Networks 1 I 2022 I Lecture 8

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Kernels I 2022 I Lecture 7