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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

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

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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

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Self-Supervised Learning

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

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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

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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 Machine Learning I Naive Bayes, Laplace Smoothing I 2022 I Lecture 6

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

Stanford CS229 Machine Learning I Exponential family, Generalized Linear Models I 2022 I Lecture 4

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

Stanford CS229 I Weighted Least Squares, Logistic regression, Newton’s Method I 2022 I Lecture 3

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

Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2

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

Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5

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