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This lecture examines structured prediction with energy-based factor graphs, including efficient inference, sequence labeling, Graph Transformer Networks, loss functions, backpropagation, neural ODEs, and variational inference.
Syllabus
– Week 14 – Lecture
– Structured Prediction, Energy based factor graphs, Sequence Labeling
– Efficient Inference for Energy-Based Factor Graph and Some Simple Energy-Based Factor Graphs
– Graph Transformer Net
– Comparing Losses and the start of language models as graphs
– Forward algorithm in Graph Transformer Networks
– Lagrangian formulation of back prop and neural ODE
– Variational Inference in terms of Energy
Taught by
Alfredo Canziani