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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

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

AI Science

Elon Musk Nick Bostrom Ray Kurzweil Superintelligence

AI Science

Ray Kurzweil – Human-Level AI is Just 12 Years Away

AI Science, Neural Network

Backpropagation calculus | Chapter 4, Deep learning

AI Science, Neural Network

What is backpropagation really doing? | Chapter 3, Deep learning

AI Science, Neural Network

Gradient descent, how neural networks learn | Chapter 2, Deep learning

AI Science, Neural Network

But what is a neural network? | Chapter 1, Deep learning

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 2.5 — What perceptrons can’t do [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]