2 citations · 2 across the 3 of their papers we have counts for
4 papers
Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds
N Alex Cayco-Gajic, Arthur Pellegrino
Similarity measures are widely used to interpret the representational geometries used by neural networks to solve tasks. Yet, because existing methods compare the extrinsic geometr…
RNNs perform task computations by dynamically warping neural representations
Arthur Pellegrino, Angus Chadwick
Analysing how neural networks represent data features in their activations can help interpret how they perform tasks. Hence, a long line of work has focused on mathematically chara…
Emergent Riemannian geometry over learning discrete computations on continuous manifolds
Julian Brandon, Angus Chadwick, Arthur Pellegrino
Many tasks require mapping continuous input data (e.g. images) to discrete task outputs (e.g. class labels). Yet, how neural networks learn to perform such discrete computations on…
Low Tensor Rank Learning of Neural Dynamics
Arthur Pellegrino, N Alex Cayco-Gajic, Angus Chadwick
Learning relies on coordinated synaptic changes in recurrently connected populations of neurons. Therefore, understanding the collective evolution of synaptic connectivity over lea…