5 papers
What's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold
Simone Azeglio, Arianna Di Bernardo
Recent advances in diffusion models have demonstrated their remarkable ability to capture complex image distributions, but the geometric properties of the learned data manifold rem…
Equivariant Representation Learning in the Presence of Stabilizers
Luis Armando Pérez Rey, Giovanni Luca Marchetti, Danica Kragic +2
We introduce Equivariant Isomorphic Networks (EquIN) -- a method for learning representations that are equivariant with respect to general group actions over data. Differently from…
Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
Tongzhou Wang, Phillip Isola
Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives and are gaining more attention in machine learning applications. Imposing such quasimetric structures in…
Improving Neural Predictivity in the Visual Cortex with Gated Recurrent Connections
Simone Azeglio, Simone Poetto, Luca Savant Aira +1
Computational models of vision have traditionally been developed in a bottom-up fashion, by hierarchically composing a series of straightforward operations - i.e. convolution and p…
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings
Simone Azeglio, Arianna Di Bernardo, Gabriele Penna +6
Electrocorticogram (ECoG) well characterizes hand movement intentions and gestures. In the present work we aim to investigate the possibility to enhance hand pose classification, i…