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

Video

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

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 I Weighted Least Squares, Logistic regression, Newton’s Method I 2022 I Lecture 3

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2

Machine Learning CS229, Self-Supervised Learning, Standford U, Standford University

Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5

Transformer Architecture

How do Vision Transformers work? – Paper explained | multi-head self-attention & convolutions

Transformer Architecture

ConvNeXt: A ConvNet for the 2020s – Paper Explained (with animations)

Transformer Architecture

Linear algebra with Transformers – Paper Explained

Transformer Architecture

Masked Autoencoders Are Scalable Vision Learners – Paper explained and animated!

Transformer Architecture

The efficiency misnomer | Size does not matter | What does the number of parameters mean in a model?

Transformer Architecture

Do Transformers process sequences of FIXED or of VARIABLE length? | #AICoffeeBreakQuiz

Transformer Architecture

Swin Transformer paper animated and explained

Transformer Architecture

How modern search engines work – Vector databases explained! | Weaviate open-source

Transformer Architecture

BERT: transfer learning for NLP