[DMANET] Special Session at ESANN 2027: Where Kernels Meet Networks

Dear colleagues, we would like to draw your attention to the following special session at ESANN 2027: WHERE KERNELS MEET NETWORKS: NEURAL TANGENT KERNELS, GAUSSIAN PROCESSES AND BEYOND KEY DATES Paper submission deadline: 18 November 2026 ESANN 2027: 21-23 April 2027, Bruges, Belgium, and online ORGANIZERS Frank-Michael Schleif Technical University of Applied Sciences Würzburg-Schweinfurt, Germany Nils M. Kriege University of Vienna, Austria Johan Suykens KU Leuven, Belgium SCOPE Kernel methods and neural networks have historically often been viewed as competing paradigms. A substantial body of recent work, however, has revealed increasingly close connections between them. Infinitely wide neural networks can be described in terms of kernels and Gaussian processes, while Neural Tangent Kernels provide an infinite-dimensional kernel perspective on the training dynamics of wide networks. At the same time, current research increasingly moves beyond fixed limiting kernels towards learnable, structured and expressive kernel models, finite-width effects, alternative infinite-width limits, and scalable computational approaches. The aim of this special session is to bring together these different perspectives and to encourage interaction between researchers working on kernel methods, Gaussian processes, neural-network theory, and related areas. TOPICS We welcome theoretical, methodological and applied contributions on topics including, but not limited to: - infinite-width, arc-cosine, compositional and deep kernels - correspondences between neural networks, Gaussian processes and Neural Tangent Kernels - finite-width effects and deviations from the NTK regime - feature learning and mean-field limits beyond lazy training - deep kernel processes and learning kernel representations from data - heavy-tailed and stable infinite-width limits - deep and restricted kernel machines - graph kernels, graph neural networks and message-passing expressivity - random features, Nyström methods, low-rank approximations and sketching - indefinite, non-metric and structured kernels - Gaussian processes for deep models, uncertainty quantification and calibration - applications to molecular, graph, scientific and other structured data SUBMISSION Submissions to special sessions follow the same review procedure, format and submission rules as regular ESANN papers. Authors should indicate the corresponding special session when submitting. Further information about the special session and the submission procedure can be found on the ESANN website: https://www.esann.org We would be very happy to see contributions addressing the increasingly rich interface between kernel learning, Gaussian processes and neural networks. Best regards, Nils M. Kriege on behalf of the session organizers (Nils M. Kriege, Johan Suykens, Frank-Michael Schleif) ********************************************************** * * Contributions to be spread via DMANET are submitted to * * DMANET@zpr.uni-koeln.de * * Replies to a message carried on DMANET should NOT be * addressed to DMANET but to the original sender. The * original sender, however, is invited to prepare an * update of the replies received and to communicate it * via DMANET. * * DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET) * http://www.zaik.uni-koeln.de/AFS/publications/dmanet/ * **********************************************************