40 citations · 90 across the 6 of their papers we have counts for
7 papers
Metriplector: From Field Theory to Neural Architecture
Dan Oprisa, Peter Toth
We present Metriplector, a neural architecture primitive in which the input configures an abstract physical system -- fields, sources, and operators -- and the dynamics of that sys…
Gated Linear Networks
Joel Veness, Tor Lattimore, David Budden +8
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distri…
Equivariant Hamiltonian Flows
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins +1
This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Lie-algebra of local symmetry transformat…
Hamiltonian Generative Networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle +3
The Hamiltonian formalism plays a central role in classical and quantum physics. Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserve…
Online Learning with Gated Linear Networks
Joel Veness, Tor Lattimore, Avishkar Bhoopchand +3
This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our meth…
Criticality & Deep Learning II: Momentum Renormalisation Group
Dan Oprisa, Peter Toth
Guided by critical systems found in nature we develop a novel mechanism consisting of inhomogeneous polynomial regularisation via which we can induce scale invariance in deep learn…