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

Video

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, Geoffrey Hinton, Neural Network, University AI

Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 9.6 — MacKay ‘s quick and dirty method [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.2 — Mixtures of Experts [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.1 — Hopfield Nets [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning]