Š. Kucharský

Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision

This work shows how auxiliary guidance losses applied to internal representations leads to faster convergence when training data is abundant and to better performance when it is scarce, and demonstrates that auxiliary supervision improves convergence speed and accuracy on a range of challenging real-world inference pro...