Latent Variable EBMs for Structured Prediction
Explore an in-depth lecture on latent variable Energy-Based Models (EBMs) for structured prediction, delivered by renowned AI researcher Yann LeCun. Dive into various topics including training EBMs, contrastive and regularized methods, general margin loss, joint embedding architectures, and generative adversarial networks (GANs). Learn about cutting-edge techniques such as Wav2Vec 2.0, XLSR for multilingual speech recognition, and non-contrastive methods like BYOL and SwAV. Discover practical applications of latent variable models, including DETR for object detection. Gain insights into structured prediction, factor graphs, and the Viterbi algorithm through detailed explanations and whiteboard demonstrations. Conclude with an exploration of graph transformer networks and their compositions, providing a comprehensive overview of advanced machine learning concepts and techniques.