This work introduces Projection agents, a GCO methodology built on the Wolpertinger framework and adapted to a shared GNN-based latent space for observations and actions, and releases LaGCO-RL, a Python library that automates latent action-space construction and supports existing RL-GCO solutions, promoting reproducibi...
F. Terranova, Guillermo Bern'ardez, Albert Cabellos-Aparicio et al.· arXiv.org· 0 citations
It is shown that spontaneous menopause is characterized by dysregulation in inflammatory, synaptic, metabolic and Alzheimer's disease biologic processes, which tracked more strongly with hormones than with age.
Madeline Wood Alexander, Jennifer S. Rabin, Michelle R. Caunca et al.· Nature Medicine· 1 citation
Overall, this work lays out an initial phenomenology of realized geometry in VLA policies and demonstrates that a concept's shape carries as much importance as its readability.
Mozes Jacobs, Aiden Swann, Mathilde Papillon et al.· 0 citations
Cluster-TNN is introduced, a domain-agnostic framework that avoids this bottleneck by lifting locally instead, and enables the first training of multiple different higher-order Topological Neural Networks on large datasets such as Reddit and OGBN Products.
David Leko, Luka Beni'c, Guillermo Bernárdez et al.· 0 citations
The representations that language models learn for concepts such as months, weekdays, and places display consistent geometric structure: circles and saddle-shaped"Pringle"manifolds. Recent work traced these structures to $\textit{translation symmetry}$ in word co-occurrence statistics, deriving the observed Fourier geo...
Liam Storan, Andreas S. Tolias, N. Miolane· 0 citations
The equivariance relaxation morphism, which preserves functionality while reparameterizing a group equivariant layer to operate with equivariance constraints on a subgroup, and the [ G ]-mixed equivariant layer, which mixes operations constrained to equivariance to different groups to enable within-layer equivariance o...
Kaitlin Maile, Dennis G. Wilson, Patrick Forré et al.· 0 citations
Topological richness grows with network size, and second homology emerges significantly above the null only in the larger networks, and results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
Eve Bodnia, M. Basart, S. Hai et al.· bioRxiv· 0 citations
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