100 citations · 221 across the 31 of their papers we have counts for
9 papers · 1 filter
The Flood Complex: Large-Scale Persistent Homology on Millions of Points
Florian Graf, Paolo Pellizzoni, Martin Uray +2
We consider the problem of computing persistent homology (PH) for large-scale Euclidean point cloud data, aimed at downstream machine learning tasks, where the exponential growth o…
Neural Persistence Dynamics
Sebastian Zeng, Florian Graf, Martin Uray +2
We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such…
Latent SDEs on Homogeneous Spaces
Sebastian Zeng, Florian Graf, Roland Kwitt
We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent…
On Measuring Excess Capacity in Neural Networks
Florian Graf, Sebastian Zeng, Bastian Rieck +2
We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empir…
Topological Attention for Time Series Forecasting
Sebastian Zeng, Florian Graf, Christoph Hofer +1
The problem of (point) forecasting time series is considered. Most approaches, ranging from traditional statistical methods to recent learning-based techniq…
Topologically Densified Distributions
Christoph D. Hofer, Florian Graf, Marc Niethammer +1
We study regularization in the context of small sample-size learning with over-parameterized neural networks. Specifically, we shift focus from architectural properties, such as no…
Deep Multi-View Learning via Task-Optimal CCA
Heather D. Couture, Roland Kwitt, J. S. Marron +3
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use…
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer, Roland Kwitt, Mandar Dixit +1
We study the problem of learning representations with controllable connectivity properties. This is beneficial in situations when the imposed structure can be leveraged upstream. I…
Graph Filtration Learning
Christoph D. Hofer, Florian Graf, Bastian Rieck +2
We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregat…