1 code implementation • 7 Jul 2023 • Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani
For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of $1. 31\times 10^5$ and $7. 13\times 10^3$ on average, and up to $7. 54\times 10^5$ and $3. 18\times 10^4$.
no code implementations • 16 Jun 2022 • Milad Kazemi, Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani, Ben Wooding
The growth bound together with the sampled trajectories are then used to construct the abstraction and synthesise a controller.
no code implementations • 21 Jan 2019 • Mahmoud Salamati, Sadegh Soudjani, Rupak Majumdar
We run CMA-ES using human participants to provide the fitness function, using the insight that the choice of best candidates in CMA-ES can be naturally modeled as a perception task: pick the top $k$ inputs perceptually closest to a fixed input.
