Differentiable Particle Filtering without Modifying the Forward Pass

Understand

Particle filters are not compatible with automatic differentiation due to the presence of discrete resampling steps.

• While known estimators for the score function, based on Fisher's identity, can be computed using particle filters, up to this point they required manual implementation.
• In this paper we show that such estimators can be computed using automatic differentiation, after introducing a simple correction to the particle weights.
• This correction utilizes the stop-gradient operator and does not modify the particle filter operation on the forward pass, while also being cheap and easy to compute.