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Found 7 papers, 2 papers with code
Equivariant Representation Learning in the Presence of Stabilizers
We introduce Equivariant Isomorphic Networks (EquIN) -- a method for learning representations that are equivariant with respect to general group actions over data.
Identifying the Sources of Uncertainty in Object Classification
In image-based object classification, the visual appearance of objects determines which class they are assigned to.
A Metric for Linear Symmetry-Based Disentanglement
We propose a metric for the evaluation of the level of LSBD that a data representation achieves.
Quantifying and Learning Linear Symmetry-Based Disentanglement
The definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD.
Quantifying and Learning Disentangled Representations with Limited Supervision
Although several works focus on learning LSBD representations, such methods require supervision on the underlying transformations for the entire dataset, and cannot deal with unlabeled data.
Complex Vehicle Routing with Memory Augmented Neural Networks
Deep Learning may provide solutions which are less time-consuming and of higher quality at large scales, as it generally does not need to generate solutions in an iterative manner, and Deep Learning models have shown a surprising capacity for solving complex tasks in recent years.
Semi-supervised Pose Estimation with Geometric Latent Representations
The amount of labelled data for pose estimation is relatively limited.
