4 papers · 1 filter
Compressed Diffusion
Scott Gigante, Jay S. Stanley, Ngan Vu +4
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most…
Interpretable Neuron Structuring with Graph Spectral Regularization
Alexander Tong, David van Dijk, Jay S. Stanley +6
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features.…
Harmonic Alignment
Jay S. Stanley, Scott Gigante, Guy Wolf +1
We propose a novel framework for combining datasets via alignment of their intrinsic geometry. This alignment can be used to fuse data originating from disparate modalities, or to…
Geometry-Based Data Generation
Ofir Lindenbaum, Jay S. Stanley, Guy Wolf +1
Many generative models attempt to replicate the density of their input data. However, this approach is often undesirable, since data density is highly affected by sampling biases,…