44 citations · 253 across the 39 of their papers we have counts for
6 papers · 1 filter
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…
Out-of-Sample Extrapolation with Neuron Editing
Matthew Amodio, David van Dijk, Ruth Montgomery +2
While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the…
MAGAN: Aligning Biological Manifolds
Matthew Amodio, Smita Krishnaswamy
It is increasingly common in many types of natural and physical systems (especially biological systems) to have different types of measurements performed on the same underlying sys…
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,…
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Scott Gigante, David van Dijk, Kevin Moon +3
Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describ…