arXiv:2607. 04800v1 Announce Type: new Abstract: Neural networks are thought to represent concepts as directions in their activation space, and superposition lets them encode more concepts than they have dimensions.
By Francisco Ferreira da Silva, Stefan Heimersheim
The paper introduces a general theoretical framework for fibrations on graphs labeled by a commutative monoid, extending the classic theory of graph fibrations to weighted and algebraically labeled graphs. It also accommodates approximate fibrations and demonstrates how this framework can be used to compress arbitrary neural networks, including CNNs, providing a solid theoretical basis for recent findings on fibration symmetries in geometric deep learning.
By Paolo Boldi
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science .
By Muhammad Ardi
arXiv:2606. 14673v1 Announce Type: new Abstract: We study whether the Compressed Computation (CC) toy model (Braun et al.
By Jai Bhagat, Sara Molas-Medina, Giorgi Giglemiani, Stefan Heimersheim
arXiv:2511. 02659v4 Announce Type: replace-cross Abstract: Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched data samples, where the sketched data are leveraged to prevent catastrophic forgetting.
By Cooper Simpson, Stephen Becker, Alireza Doostan
arXiv:2606. 18538v1 Announce Type: new Abstract: One of the major difficulties in the mechanistic interpretability of neural networks is the occurrence of polysemanticity, which suggests that each neuron is typically responsible for multiple different tasks, impeding a clean interpretation of their function.
By Mriganka Basu Roy Chowdhury, Eric McLaughlin Weiner