Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matchingComment: ECCV 2020 conference pape

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs

Structural, morphological and electrical properties of Cu2ZnSn1-xSixS4 (x = 0.8, x = 1) for solar-cells applications

International audienc

Substitution of Li for Cu in Cu 2 ZnSnS 4 : Toward Wide Band Gap Absorbers with Low Cation Disorder for Thin Film Solar Cells

International audienc

Effect of the chemical composition of co-sputtered Zn(O,S) buffer layers on Cu(In,Ga)Se 2 solar cell performance

International audienc

Electrical properties of Cu 2 Zn(Sn 1−x Si x )S 4 ( x = 0.1, x = 0.4) compounds for absorber materials in solar-cells

International audienc

Leveraging Reinforcement Learning, Constraint Programming and Local Search: A Case Study in Car Manufacturing

International audienceThe problem of transporting vehicle components in a car manufacturer workshop can be seen as a large scale single vehicle pickup and delivery problem with periodic time windows. Our experimental evaluation indicates that a relatively simple constraint model shows some promise and in particular outperforms the local search method currently employed at Renault on industrial data over long time horizon. Interestingly, with an adequate heuristic, constraint propagation is often sufficient to guide the solver toward a solution in a few backtracks on these instances. We therefore propose to learn efficient heuristic policies via reinforcement learning and to leverage this technique in several approaches: rapid-restarts, limited discrepancy search and multi-start local search. Our methods outperform both the current local search approach and the classical CP models on industrial instances as well as on synthetic data