# Neural Sequence Transformation, August 2021

Sabyasachi Mukherjee, Sayan Mukherjee, Binh-Son Hua, Nobuyuki Umetani, Daniel Meister, August 2021

We consider the problem of accelerating sequences of Monte-Carlo estimates of a finite integral, which has a slow convergence rate. We achieve faster convergence with the help of sequence transformation: by changing the sequence of Monte-Carlo estimates into another sequence that converges faster. Demonstrating the difficulties of doing this analytically, we use a data-driven approach that works well for several 1D integrals as well as scenes in light transport simulation.

Pacific Graphics (accepted)