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

Video

Self-Supervised Learning

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

February 20, 2024

Video

Self-Supervised Learning

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

February 20, 2024

Video

Self-Supervised Learning

Evrim Acar – Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

February 20, 2024

Video

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 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 13.1 — The ups and downs of backpropagation [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 14.1 — Learning layers of features by stacking RBMs [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 14.2 — Discriminative learning for DBNs [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 14.3 — Discriminative fine tuning [Neural Networks for Machine Learning]