Conférences  >  Informatique  >  Algorithmes et structures de données

Réseaux de neurones et intelligence artificielle, apprentissage automatique,    Mise en œuvre et applications de l'intelligence artificielle

Society for Industrial and Applied Mathematics (SIAM)

Society for Industrial and Applied Mathematics (SIAM)

Society for Industrial and Applied Mathematics (SIAM)

The International Conference on Algorithms and Discrete Applied Mathematics (CALDAM), held under the aegis of the Association of Computer Science and Discrete Mathematics (ACSDM), is intended to bring together researchers working in the areas of Algorithms and Applied Discrete Mathematics and provide a high-quality forum for the dissemination and discussion of research results in these broad areas.

Mathématiques appliquées (en général),    Mathématiques numériques

Algorithms and Logic Group @ University of Electronic Science and Technology of China , Theoretical Computer Science Committee of China Computer Federation (CCF)

Mathématiques appliquées (en général),    Mathématiques numériques

USENIX Association

SREcon is a gathering of engineers who care deeply about site reliability, systems engineering, and working with complex distributed systems at scale. Our purpose is to be inclusive as we bring together ideas representative of our diverse community, whether its members are focusing on a global scale, launching new products and ideas for a small business, or pivoting their approach to unite software and systems engineering. SREcon challenges both those new to the profession as well as those who have been involved in it for decades. The conference has a culture of critical thought, deep technical insights, continuous improvement, and innovation.

Génie logiciel,    Superordinateur et ordinateur parallèle

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

A long-standing question in theoretical computer science is to offer analysis tools for improving performance on typical everyday, non-worst-case instances. A recent model addressing this goal is the algorithms-with-predictions model: each problem instance comes with a possibly error-prone prediction, and the worst-case running time is given as a function of the error in that prediction. This model reflects how recent advances in machine learning are able to make reasonably good predictions on practical ‒ even very complex ‒ datasets. The algorithms-with-predictions model has been used to give strong approximation-ratio guarantees for fundamental online algorithms like scheduling and caching. How to leverage predictions to speed up running time of offline algorithms and data structures has received less attention. This Dagstuhl Seminar aims to bring together researchers from the data structures, combinatorial optimization and learned predictions communities to address the challenges of adopting learned predictions for improving running time guarantees.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Computational geometry is concerned with the design, analysis, and implementation of algorithms for geometric and topological problems, which arise naturally in a wide range of areas, including computer graphics, CAD, robotics, computer vision, image processing, spatial databases, GIS, molecular biology, sensor networks, machine learning, data mining, scientific computing, theoretical computer science, and pure mathematics. Computational geometry is a vibrant and mature field of research, with several dedicated international conferences and journals and strong intellectual connections with other computing and mathematics disciplines. The emphasis of the Dagstuhl Seminar is on presenting recent developments in computational geometry, as well as identifying new challenges, opportunities, and connections to other fields of computing.

The general topic of the conference is the theory and application of discrete ​structures. Its goal is to highlight the most salient trends in the field, which has ​close links to diverse areas such as cryptography, computer science, large-scale ​networks and biology. Particular areas of interest include: graphs and digraphs, ​hypergraphs, matroids, ordered sets, designs, coding theory, enumeration, ​combinatorics of words, discrete optimization, discrete and computational ​geometry, lattice point enumeration, combinatorial algorithms, computational ​complexity, and applications of discrete and algorithmic mathematics, including ​(but not limited to) web graphs, computational biology, telecommunication ​networks, and information processing.

Mathématiques appliquées (en général),    Mathématiques numériques

Trier University, Computer Science Department

WG conferences aim to connect theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. This conference series has a long tradition and bridges discrete mathematics and computer science. WG is mainly concerned with efficient algorithms of various types (e.g., sequential, parallel, distributed, randomized, parameterized) for problems on graphs and networks. The goal is to present recent results and to identify and explore directions for future research.

PC Chairs;     Tél.: [(not so relevant)];     Email.: fernau@uni-trier.de; kindermann@uni-trier.de

design and analysis of sequential, parallel, randomized, parameterized algorithms, distributed graph and network algorithms, structural graph theory with algorithmic or complexity applications, computational complexity of graph and network problems, graph grammars, graph rewriting systems and graph modeling, graph drawing and layouts, computational geometry, computational biology, graph mining, random graphs and models of the web and scale-free networks, support of the above concepts by suitable implementations and applications.

Trier University, Computer Science Department

WG conferences aim to connect theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. This conference series has a long tradition and bridges discrete mathematics and computer science. WG is mainly concerned with efficient algorithms of various types (e.g., sequential, parallel, distributed, randomized, parameterized) for problems on graphs and networks. The goal is to present recent results and to identify and explore directions for future research.

PC Chairs;     Tél.: [(not so relevant)];     Email.: fernau@uni-trier.de; kindermann@uni-trier.de

design and analysis of sequential, parallel, randomized, parameterized algorithms, distributed graph and network algorithms, structural graph theory with algorithmic or complexity applications, computational complexity of graph and network problems, graph grammars, graph rewriting systems and graph modeling, graph drawing and layouts, computational geometry, computational biology, graph mining, random graphs and models of the web and scale-free networks, support of the above concepts by suitable implementations and applications.

International Association of Computer Science and Information Technology

The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025) will be held in Kyoto, Japan during July 25-27, 2025. MLMI 2025 is organized by Ritsumeikan University, Japan.

Email.: aimee@iacsit.net

Artificial Neural Networks; Genetic Algorithms; Machine Learning; Automata, Logic and Games; Intelligent Systems; Bayesian Networks; Logic and Proof; Commercial Software;

Réseaux de neurones et intelligence artificielle, apprentissage automatique,    Conception et gestion des bases de données, Moteurs de recherche et extraction de connaissances à pa

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Constraint optimization is today a de facto practical approach to efficiently solving various types of NP-hard optimization problems arising from real-world settings. This is primarily due to significant advances in practical algorithms and implementation-level techniques, which have resulted in increasingly efficient and robust constraint optimization solvers for a range of NP-hard declarative languages. However, the need for even more efficient and robust constraint optimization solvers continues to grow, as in several contexts more and more complex optimization problems need to be solved.