Space-filling designs such as Low Discrepancy Sequence (LDS), Latin Hypercube Sampling (LHS) and Jittered Sampling (JS) were proposed for fully parallel hyperparameter search, and were shown to be more effective than random and grid search. We prove that LHS and JS outperform random search only by a constant factor. Consequently, we introduce a new sampling approach based on the reshaping of the search distribution, and we show both theoretically and numerically that it leads to significant gains over random search. Two methods are proposed for the reshaping: Recentering (when the distribution of the optimum is known), and Cauchy transformation (when the distribution of the optimum is unknown). The proposed methods are first validated on artificial experiments and simple real-world tests on clustering and Salmon mappings. Then we demonstrate that they drive performance improvement in a wide range of expensive artificial intelligence tasks, namely attend/infer/repeat, video next frame segmentation forecasting and progressive generative adversarial networks.

Multi-Modal Optimization problems are widespread and can be solved using numerous methods, such as niching, sharing or clearing. In this paper, we are interested in algorithms based on restart strategies, where the searching point is restarted at another initial position when an optimum is found. Previous works show that the choice of these initial positions greatly impacts the performance of the algorithm but is not easy to make. In this paper, we propose a new restart strategy, based on reinforcement learning. Our algorithm subdivides the search space and uses a Multi-Armed Bandit technique to choose the successive restart positions. We experiment this algorithm on various functions and on a modified Hump function with more complex local areas. Our results show significant improvements over previous algorithms, such as the Quasi-Random restart with Decreasing Step-size algorithm.

Ma and Sonka proposed a fully parallel 3D thinning algorithm which does not always preserve topology. We propose an algorithm based on P-simple points which automatically corrects Ma and Sonka's algorithm. As far as we know, our algorithm is the only fully parallel curve thinning algorithm which preserves topology.

Catopsys is a low-cost projection system aiming at making mixed reality (virtual, augmented or diminished reality) affordable. It combines a videoprojector, a camera and a convex mirror and works in a non-specific room. This system displays an immersive environment by projecting an image onto the different parts of the room. However, the presence of an uncalibrated projector, heterogeneous materials and light inter-reflections influence the colors of the environment displayed in the room. Radiometric compensation of the projection process enables the system to reduce this problem.