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 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 5.4 — Convolutional nets for object recognition [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 6.3 — The momentum method [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 6.4 — Adaptive learning rates for each connection [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 6.5 — Rmsprop: normalize the gradient [Neural Networks for Machine Learning]

AI Science, Geoffrey Hinton, Neural Network, University AI

Lecture 7.1 — Modeling sequences: a brief overview [Neural Networks for Machine Learning]