'Riemannian Bilevel Optimization', by Jiaxiang Li, Shiqian Ma.
http://jmlr.org/papers/v26/24-0397.html
#riemannian #optimization #bilevel
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'Learning Discretized Neural Networks under Ricci Flow', by Jun Chen, Hanwen Chen, Mengmeng Wang, Guang Dai, Ivor W. Tsang, Yong Liu.
http://jmlr.org/papers/v25/22-0444.html
#gradients #gradient #riemannian
Next week, the school of our thematic programme on #Geometry beyond #Riemann: #Curvature and #Rigidity is coming. 
Hope everyone is registered who is into #Riemannian and #Lorentzian #conemanifolds, #Hilbert and #Finsler #metrics!
Find out more about the schedule here. 
https://www.esi.ac.at/events/e477/
Click for the video here: https://twitter.com/ESIVienna/status/1702317593401651693
www.esi.ac.atActivitiesThe Erwin Schroedinger International Institute For Mathematics and Physics
Continued thread
#Language #models. For example, in #NLP, when #training
recurrent #neural #networks, it is useful to constraint the transition #matrix to be #unitary (Arjovsky et al., 2015). The unitary
matrix keeps the #gradient #norm unchanged, and the network
is able to learn long-range dependencies. Unitary matrices
form a #smooth #Riemannian #manifold, and Riemannian #optimization can be easily applied to them.
https://arxiv.org/pdf/2005.02819
Geodesic Clustering in Deep Generative Models
https://arxiv.org/abs/1809.04747
Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson
Action editor: Rianne van den Berg.
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami
Action editor: Jasper Snoek.
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Diffusion Models for Constrained Domains
'Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds', by Didong Li, Wenpin Tang, Sudipto Banerjee.
http://jmlr.org/papers/v24/22-0503.html
#covariograms #riemannian #covariogram
'When Locally Linear Embedding Hits Boundary', by Hau-Tieng Wu, Nan Wu.
http://jmlr.org/papers/v24/21-0697.html
#embedding #manifold #riemannian
Identifying latent distances with Finslerian geometry
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction
Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices...
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
