
SymmetryBased Disentangled Representation Learning requires Interaction with Environments
Finding a generally accepted formal definition of a disentangled represe...
read it

Invariantequivariant representation learning for multiclass data
Representations learnt through deep neural networks tend to be highly in...
read it

Towards a Definition of Disentangled Representations
How can intelligent agents solve a diverse set of tasks in a dataeffici...
read it

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
read it

A Metric for Linear SymmetryBased Disentanglement
The definition of Linear SymmetryBased Disentanglement (LSBD) proposed ...
read it

Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an import...
read it

Fiducial Symmetry in Action
Symmetry is key in classical and modern physics. A striking example is c...
read it
Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learning expressive densities that are invariant with respect to a known Liealgebra of local symmetry transformations while providing an equivariant representation of the data. We provide proof of principle demonstrations of how such flows can be learnt, as well as how the addition of symmetry invariance constraints can improve data efficiency and generalisation. Finally, we make connections to disentangled representation learning and show how this work relates to a recently proposed definition.
READ FULL TEXT
Comments
There are no comments yet.