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Found 8 papers, 2 papers with code
Engineering flexible machine learning systems by traversing functionally-invariant paths
We conceptualize the weight space of a neural network as a curved Riemannian manifold equipped with a metric tensor whose spectrum defines low rank subspaces in weight space that accommodate network adaptation without loss of prior knowledge.
Traversing Geodesics to Grow Biological Structures
Biological tissues reliably grow into precise, functional structures from simple starting states during development.
Solving hybrid machine learning tasks by traversing weight space geodesics
Broadly, we introduce a geometric framework that unifies a range of machine learning objectives and that can be applied to multiple classes of neural network architectures.
Sparsifying networks by traversing Geodesics
The geometry of weight spaces and functional manifolds of neural networks play an important role towards 'understanding' the intricacies of ML.
Architecture Agnostic Neural Networks
Within this sparse, binary paradigm we sample many binary architectures to create families of architecture agnostic neural networks not trained via backpropagation.
Self-organization of multi-layer spiking neural networks
Inspired by this strategy, we attempt to efficiently self-organize large neural networks with an arbitrary number of layers into a wide variety of architectures.
Geometric algorithms for predicting resilience and recovering damage in neural networks
Biological neural networks have evolved to maintain performance despite significant circuit damage.
Neural networks grown and self-organized by noise
The algorithm is adaptable to a wide-range of input-layer geometries, robust to malfunctioning units in the first layer, and so can be used to successfully grow and self-organize pooling architectures of different pool-sizes and shapes.
