5 citations · 6 across the 3 of their papers we have counts for
4 papers
Optimizing Memory Mapping Using Deep Reinforcement Learning
Pengming Wang, Mikita Sazanovich, Berkin Ilbeyi +16
Resource scheduling and allocation is a critical component of many high impact systems ranging from congestion control to cloud computing. Finding more optimal solutions to these p…
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov +1
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given syste…
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin +1
In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulati…
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong +6
We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of preference for most self-driving vehicles. We argue that, by leveraging real data, we…