100 citations · 148 across the 6 of their papers we have counts for
6 papers
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…
Dissecting Supervised Contrastive Learning
Florian Graf, Christoph D. Hofer, Marc Niethammer +1
Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervise…
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…
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…
Deep Learning with Topological Signatures
Christoph Hofer, Roland Kwitt, Marc Niethammer +1
Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persist…