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Found 6 papers, 3 papers with code
On the curvature of the loss landscape
In this work, we consider the loss landscape as an embedded Riemannian manifold and show that the differential geometric properties of the manifold can be used when analyzing the generalization abilities of a deep net.
Identifying latent distances with Finslerian geometry
Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well.
PyRelationAL: a python library for active learning research and development
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data through strategically querying new data points that are the most useful for a particular task.
Pulling back information geometry
Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models.
Density estimation on smooth manifolds with normalizing flows
We present a framework for learning probability distributions on topologically non-trivial manifolds, utilizing normalizing flows.
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply.
