100 citations · 221 across the 31 of their papers we have counts for
34 papers
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…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…
CARL: A Framework for Equivariant Image Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +3
Image registration estimates spatial correspondences between a pair of images. These estimates are typically obtained via numerical optimization or regression by a deep network. A…
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…
uniGradICON: A Foundation Model for Medical Image Registration
Lin Tian, Hastings Greer, Roland Kwitt +5
Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generi…
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…
Inverse Consistency by Construction for Multistep Deep Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +5
Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a con…
: Approximate Diffeomorphisms via Gradient Inverse Consistency
Lin Tian, Hastings Greer, François-Xavier Vialard +6
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration tec…
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…
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study
Leonard E. van Dyck, Roland Kwitt, Sebastian J. Denzler +1
Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Rece…
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…
ICON: Learning Regular Maps Through Inverse Consistency
Hastings Greer, Roland Kwitt, Francois-Xavier Vialard +1
Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformat…
Sparse Pose Trajectory Completion
Bo Liu, Mandar Dixit, Roland Kwitt +2
We propose a method to learn, even using a dataset where objects appear only in sparsely sampled views (e.g. Pix3D), the ability to synthesize a pose trajectory for an arbitrary re…
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…
A Shooting Formulation of Deep Learning
François-Xavier Vialard, Roland Kwitt, Susan Wei +1
Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE). Altho…
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…
Metric Learning for Image Registration
Marc Niethammer, Roland Kwitt, Francois-Xavier Vialard
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. Howe…
Stochastic Block Models with Multiple Continuous Attributes
Natalie Stanley, Thomas Bonacci, Roland Kwitt +2
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In t…
Feature Space Transfer for Data Augmentation
Bo Liu, Xudong Wang, Mandar Dixit +2
The problem of data augmentation in feature space is considered. A new architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for the modeling of feature trajecto…
Fast Predictive Simple Geodesic Regression
Zhipeng Ding, Greg Fleishman, Xiao Yang +3
Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datas…
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Xu Han, Roland Kwitt, Stephen Aylward +6
Brain extraction from images is a common pre-processing step. Many approaches exist, but they are frequently only designed to perform brain extraction from images without strong pa…