10 citations · 19 across the 9 of their papers we have counts for
7 papers · 1 filter
Spontaneous symmetry breaking and Goldstone modes for deep information propagation
Nabil Iqbal, T. Anderson Keller, Yue Song +2
In physical systems, whenever a continuous symmetry is spontaneously broken, the system possesses excitations called Goldstone modes, which allow coherent information propagation o…
Kernel-Gradient Drifting Models
Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3
We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting models with directions induced by…
Krause Synchronization Transformers
Jingkun Liu, Yisong Yue, Max Welling +1
Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When composed across depth, this interacti…
Langevin Flows for Modeling Neural Latent Dynamics
Yue Song, T. Anderson Keller, Yisong Yue +2
Neural populations exhibit latent dynamical structures that drive time-evolving spiking activities, motivating the search for models that capture both intrinsic network dynamics an…
Unsupervised Representation Learning from Sparse Transformation Analysis
Yue Song, Thomas Anderson Keller, Yisong Yue +2
There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality, controllability, or symmetry. In this pape…
Flow Factorized Representation Learning
Yue Song, T. Anderson Keller, Nicu Sebe +1
A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation. Th…
Topographic VAEs learn Equivariant Capsules
T. Anderson Keller, Max Welling
In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method fo…