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06/30/2023
Geometric Autoencoders – What You See is What You Decode
Visualization is a crucial step in exploratory data analysis. One possib...
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10/14/2022
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
Branched Optimal Transport (BOT) is a generalization of optimal transpor...
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06/03/2022
Contrastive learning unifies t-SNE and UMAP
Neighbor embedding methods t-SNE and UMAP are the de facto standard for ...
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05/17/2022
CellTypeGraph: A New Geometric Computer Vision Benchmark
Classifying all cells in an organ is a relevant and difficult problem fr...
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10/05/2021
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice
The minimum graph cut and minimum s-t-cut problems are important primiti...
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03/26/2021
UMAP does not reproduce high-dimensional similarities due to negative sampling
UMAP has supplanted t-SNE as state-of-the-art for visualizing high-dimen...
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11/26/2020
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons
Instance segmentation of overlapping objects in biomedical images remain...
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09/10/2020
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
This work introduces a new proposal-free instance segmentation method th...
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02/28/2020
Instance Separation Emerges from Inpainting
Deep neural networks trained to inpaint partially occluded images show a...
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12/29/2019
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation
Semantic instance segmentation is the task of simultaneously partitionin...
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11/06/2019
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
The seeded Watershed algorithm / minimax semi-supervised learning on a g...
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08/21/2019
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis
Calcium imaging is one of the most important tools in neurophysiology as...
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06/27/2019
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
We propose a novel theoretical framework that generalizes algorithms for...
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06/27/2019
Deep Active Learning with Adaptive Acquisition
Model selection is treated as a standard performance boosting step in ma...
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05/22/2019
End-to-End Learned Random Walker for Seeded Image Segmentation
We present an end-to-end learned algorithm for seeded segmentation. Our ...
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04/25/2019
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning
Image partitioning, or segmentation without semantics, is the task of de...
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06/22/2018
On the Spectral Bias of Deep Neural Networks
It is well known that over-parametrized deep neural networks (DNNs) are ...
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05/19/2018
Sampling-Free Variational Inference of Bayesian Neural Nets
We propose a new Bayesian Neural Net (BNN) formulation that affords vari...
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03/15/2018
Diverse M-Best Solutions by Dynamic Programming
Many computer vision pipelines involve dynamic programming primitives su...
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03/02/2018
Essentially No Barriers in Neural Network Energy Landscape
Training neural networks involves finding minima of a high-dimensional n...
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12/07/2017
End-to-end Learning of Deterministic Decision Trees
Conventional decision trees have a number of favorable properties, inclu...
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11/20/2017
Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction...
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10/16/2012
Active Learning with Distributional Estimates
Active Learning (AL) is increasingly important in a broad range of appli...
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09/30/2010