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X-WR-CALDESC:Events for Erlangen AI Hub
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DTSTART;TZID=Europe/London:20260430T130000
DTEND;TZID=Europe/London:20260430T140000
DTSTAMP:20260430T174912
CREATED:20260413T182326Z
LAST-MODIFIED:20260417T164508Z
UID:876-1777554000-1777557600@erlangenhub.ox.ac.uk
SUMMARY:Erlangen AI Hub Seminar: Geometry\, Complexity\, and Generalization in Learning Systems
DESCRIPTION:Compression-based complexity measures have been used to construct non-vacuous generalization bounds for deep neural networks. In this talk\, Branton DeMoss (University of Oxford) will discuss the relationship between compression\, complexity\, and the geometry of the loss landscape. Using a geometric complexity measure to track memorization and generalization in some pathological deep learning phenomena like grokking and double descent\, and refuting a common criticism of sharpness-based generalization measures based on their lack of parameterization-invariance. \nRegister here: Erlangen AI Hub Seminar: Geometry\, Complexity\, and Generalization in Learning Systems
URL:https://erlangenhub.ox.ac.uk/event/erlangen-ai-hub-seminar-geometry-complexity-and-generalization-in-learning-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260416T130000
DTEND;TZID=Europe/London:20260416T140000
DTSTAMP:20260430T174912
CREATED:20260413T182649Z
LAST-MODIFIED:20260413T182732Z
UID:877-1776344400-1776348000@erlangenhub.ox.ac.uk
SUMMARY:Erlangen AI Hub Seminar: Estimating Intrinsic Dimensionality with L2N2
DESCRIPTION:Estimating the intrinsic dimensionality (ID) of data is a fundamental problem in machine learning and computer vision\, as it reveals the true degrees of freedom underlying high-dimensional observations. In this talk\, Eng-Jon Ong (QMUL) introduces L2N2\, a simple yet powerful ID estimator based on nearest-neighbour distance ratios that achieves state-of-the-art performance with minimal computational overhead.\n  \nWe present a theoretical framework showing that L2N2 is universal\, in the sense that it provably converges to the true intrinsic dimensionality independently of the underlying data distribution. We complement this analysis with extensive experiments on synthetic benchmark manifolds\, demonstrating strong empirical performance.\nBeyond controlled settings\, we show how L2N2 can be applied to real-world data\, including estimating the intrinsic dimensionality of datasets such as MNIST via autoencoder representations. Finally\, we illustrate how L2N2 provides new insights into deep learning by tracking how the intrinsic dimensionality of neural network layer representations evolves during training\, particularly on the phenomenon of grokking. \nRegister here: Erlangen AI Hub Seminar: Estimating Intrinsic Dimensionality with L2N2 (Eng-Jon Ong)
URL:https://erlangenhub.ox.ac.uk/event/erlangen-ai-hub-seminar-estimating-intrinsic-dimensionality-with-l2n2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260323T110000
DTEND;TZID=Europe/London:20260323T120000
DTSTAMP:20260430T174912
CREATED:20260212T144224Z
LAST-MODIFIED:20260323T083553Z
UID:820-1774263600-1774267200@erlangenhub.ox.ac.uk
SUMMARY:Erlangen Hub Seminar: Riemannian Neural Optimal Transport\, Alessandro Micheli
DESCRIPTION:Computational optimal transport (OT) provides a principled framework for generative modelling. Neural OT methods learn transport maps from data using neural networks and can be evaluated out of sample after training; however\, existing approaches are largely restricted to Euclidean settings. Extending neural OT to high-dimensional Riemannian manifolds presents significant theoretical and computational challenges. \nIn this talk\, Alessandro Micheli will show that discretisation-based OT methods on manifolds inherently face severe dimensionality scaling limitations. To address this\, he introduces Riemannian Neural Optimal Transport (RNOT)\, a continuous neural parameterisation of OT maps that avoids discretisation and incorporates geometric structure directly. Under mild regularity assumptions\, RNOT achieves sub-exponential complexity in the manifold dimension. Empirical results on synthetic and real datasets demonstrate improved scalability and competitive performance relative to existing approaches. \nRegister your interest now: \nRegistration for this event has now closed.
URL:https://erlangenhub.ox.ac.uk/event/erlangen-hub-seminar-riemannian-neural-optimal-transport/
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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260309T110000
DTEND;TZID=Europe/London:20260309T120000
DTSTAMP:20260430T174912
CREATED:20260216T141716Z
LAST-MODIFIED:20260308T170842Z
UID:836-1773054000-1773057600@erlangenhub.ox.ac.uk
SUMMARY:Erlangen Hub Seminar: Computing Diffusion Geometry\, Iolo Jones
DESCRIPTION:Calculus and geometry are ubiquitous in the theoretical modelling of scientific phenomena\, but have historically been very challenging to apply directly to real data as statistics. Diffusion geometry is a new theory that reformulates classical calculus and geometry in terms of a diffusion process\, allowing these theories to generalise beyond manifolds and be computed from data. \nIn this talk\, Iolo Jones will describe a new\, simple computational framework for diffusion geometry that substantially broadens its practical scope and improves its precision\, robustness to noise\, and computational complexity. He and colleagues present a range of new computational methods\, including all the standard objects from vector calculus and Riemannian geometry\, and apply them to solve spatial PDEs and vector field flows\, find geodesic (intrinsic) distances\, curvature\, and several new topological tools like de Rham cohomology\, circular coordinates\, and Morse theory. These methods are data-driven\, scalable\, and can exploit highly optimised numerical tools for linear algebra. \nRegistration for this event has now closed.
URL:https://erlangenhub.ox.ac.uk/event/erlangen-hub-seminar-computing-diffusion-geometry/
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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260123T150000
DTEND;TZID=Europe/London:20260123T160000
DTSTAMP:20260430T174912
CREATED:20251215T144719Z
LAST-MODIFIED:20260107T142313Z
UID:680-1769180400-1769184000@erlangenhub.ox.ac.uk
SUMMARY:Erlangen Seminar Series: Studying the Geometry of Loops on the Möbius Band
DESCRIPTION:Date: Friday 23 January\, from 3–4pm \nLocation:  Online \nTitle:  Studying the Geometry of Loops on the Mobius Band \nFrom contours of 2D shapes to knotted proteins\, many areas of statistics and physical sciences contend with data in the form of geometric loops — an embedding of S1 into a Euclidean space. Similar to many problems in geometric deep learning\, we desire a representation of the geometry of the loop that is invariant under Euclidean symmetries\, and indifferent to how the loop is parametrised. In this joint work with James Binnie (Cardiff) and Otto Sumray (MPI-Dresden)\, we propose a novel representation of a loop’s geometry by representing the distance matrix of the loop as a Morse function on a Möbius band — the two point configuration space of S1. By considering the persistent homology of this function\, we obtain a representation of the loop’s geometry that has the desired symmetry invariance. We show that this feature map is stable with respect to perturbations\, and give a geometric interpretation of the features obtained. \nTo join us on Teams please fill out the following registration link: https://forms.office.com/e/MPBYstFWun
URL:https://erlangenhub.ox.ac.uk/event/erlangen-seminar-series-studying-the-geometry-of-loops-on-the-mobius-band/
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