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29 Results
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Workshop
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Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations Richard Bergna · Sergio Calvo Ordoñez · Felix Opolka · Pietro Lió · José Miguel Hernández-Lobato |
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Riemannian Black Box Variational Inference Mykola Lukashchuk · Wouter Nuijten · Dmitry Bagaev · Ismail Senoz · Bert de Vries |
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Amortizing intractable inference in diffusion models for Bayesian inverse problems Siddarth Venkatraman · Moksh Jain · Luca Scimeca · Minsu Kim · Marcin Sendera · Mohsin Hasan · Luke Rowe · Sarthak Mittal · Pablo Lemos · Emmanuel Bengio · Alexandre Adam · Jarrid Rector-Brooks · Yashar Hezaveh · Laurence Perreault-Levasseur · Yoshua Bengio · Glen Berseth · Nikolay Malkin |
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Gradient-free variational learning with conditional mixture networks Conor Heins · Hao Wu · Dimitrije Markovic · Alexander Tschantz · Jeff Beck · Christopher L Buckley |
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Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data David Schnurr · Kai Helli · Noah Hollmann · Samuel Müller · Frank Hutter |