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Found 7 papers, 4 papers with code
Adaptive behavior with stable synapses
Improvement in behavioral performances are usually associated in machine learning and reinforcement learning to synaptic plasticity, and, in general, to changes and optimization of network parameters.
Continuous normalizing flows on manifolds
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions.
Neural Ordinary Differential Equations on Manifolds
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions.
Reparameterizing Distributions on Lie Groups
Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups.
Topological Constraints on Homeomorphic Auto-Encoding
When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic when restricted to the manifold, so that it is bijective and continuous with a continuous inverse.
Explorations in Homeomorphic Variational Auto-Encoding
Our experiments show that choosing manifold-valued latent variables that match the topology of the latent data manifold, is crucial to preserve the topological structure and learn a well-behaved latent space.
Hyperspherical Variational Auto-Encoders
But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure.
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