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)


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blueqat research
Quantum Computing, Machine Learning and Graph Theory Research Lab contact: research@blueqat.com
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