Delta-AI: Local objectives for amortized inference in sparse graphical models
A new algorithm for amortized inference in sparse probabilistic graphical models (PGMs) is presented that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably.
J. Falet, Haebeom Lee, Esmeralda S. Whitammer et al.
· International Conference on... · 9 citations