112 citations · 389 across the 35 of their papers we have counts for
7 papers · 1 filter
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations
Jan Zuiderveld, Marco Federici, Erik J. Bekkers
The high temporal resolution of audio and our perceptual sensitivity to small irregularities in waveforms make synthesizing at high sampling rates a complex and computationally int…
ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups
Hugo Aguettaz, Erik J. Bekkers, Michaël Defferrard
We introduce ChebLieNet, a group-equivariant method on (anisotropic) manifolds. Surfing on the success of graph- and group-based neural networks, we take advantage of the recent de…
Interpretable ECG classification via a query-based latent space traversal (qLST)
Melle B. Vessies, Sharvaree P. Vadgama, Rutger R. van de Leur +4
Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart. Interpretation of ECG signals to detect various abnor…
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
David M. Knigge, David W. Romero, Erik J. Bekkers
Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. In this work, we inves…
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak +3
When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kern…
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol +2
Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivarian…