In this paper we introduce a mesh approximation method that uses a volume-based metric. After a geometric simplification, we minimize the volume between the simplified mesh and th...
Pierre Alliez, Nathalie Laurent, Henri Sanson, Fra...
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
In this paper, we extend the adjoint error correction of Pierce and Giles [SIAM Review, 42 (2000), pp. 247-264] for obtaining superconvergent approximations of functionals to Gale...
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update st...
Symmetry reduction techniques can help to combat the state space explosion problem for model checking, but are restricted by the hard problem of determining equivalence of states d...