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

Video

Self-Supervised Learning

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

February 20, 2024

Video

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 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 2.2 — Perceptrons: first-generation neural networks [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 3.1 — Learning the weights of a linear neuron [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 4.1 — Learning to predict the next word [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]