#riemannian — Public Fediverse posts

This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.

📅 When: Wednesday, August 13, 1:00 – 4:00 pm
📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall
📋 What: Poster B152

• It leverages advanced mathematical techniques to better understand and classify mental workloads, offering new insights into cognitive processes and potential applications in various fields such as neuroscience, psychology, and human-computer interaction.
• By utilizing Riemannian geometry, this research provides a robust framework for analyzing spatial effects in mental workload, paving the way for more accurate and efficient classification methods. This contribution not only advances our theoretical understanding but also has practical implications for improving mental workload assessment and management.

See you there! 🚀

https://laurentperrinet.github.io/publication/choplin-25-ccn/

👏 CNRS @cnrs - Aix-Marseille University - ONERA, The French Aerospace Lab CNRS

#CCN2025 #Mental #Workload #MentalWorkload #Riemannian #Manifold

'Riemannian Bilevel Optimization', by Jiaxiang Li, Shiqian Ma.

http://jmlr.org/papers/v26/24-0397.html

#riemannian #optimization #bilevel

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/

@univienna

Click for the video here: https://twitter.com/ESIVienna/status/1702317593401651693

#Language #models. For example, in #NLP, when #training recurrent #neural #networks, it is useful to constraint the transition #matrix to be #unitary. 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

Diffusion Models for Constrained Domains

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

Action editor: Rianne van den Berg.

https://openreview.net/forum?id=xuWTFQ4VGO

#diffusion #denoising #riemannian

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Action editor: Jasper Snoek.

https://openreview.net/forum?id=dXAuvo6CGI

#langevin #stochastic #riemannian

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Action editor: Jasper Snoek.

https://openreview.net/forum?id=dXAuvo6CGI

#langevin #stochastic #riemannian

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Action editor: Jasper Snoek.

https://openreview.net/forum?id=dXAuvo6CGI

#langevin #stochastic #riemannian

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Action editor: Jasper Snoek.

https://openreview.net/forum?id=dXAuvo6CGI

#langevin #stochastic #riemannian

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Hanlin Yu, Marcelo Hartmann, Bernardo Williams, Arto Klami

Action editor: Jasper Snoek.

https://openreview.net/forum?id=dXAuvo6CGI

#langevin #stochastic #riemannian