You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • ECCV 2020 • Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht

Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.

no code implementations • 30 Jun 2023 • Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht

Visualization is a crucial step in exploratory data analysis.

no code implementations • 8 May 2023 • Roman Remme, Tobias Kaczun, Maximilian Scheurer, Andreas Dreuw, Fred A. Hamprecht

We here set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn-Sham density functional theory.

1 code implementation • 14 Oct 2022 • Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht

We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in $\mathbb{R}^2$.

2 code implementations • 3 Jun 2022 • Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak

We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings.

2 code implementations • CVPR 2022 • Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht

Classifying all cells in an organ is a relevant and difficult problem from plant developmental biology.

Ranked #1 on Node Classification on CellTypeGraph Benchmark

1 code implementation • NeurIPS 2021 • Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht

We propose the "Directed Probabilistic Watershed", an extension of the Probabilistic Watershed algorithm to directed graphs.

no code implementations • ICCV 2021 • Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht

However, we then present a simple new algorithm for seeded segmentation / graph-based semi-supervised learning that is closely based on Karger's original algorithm, showing that for these problems, extensions of Karger's algorithm can be useful.

1 code implementation • NeurIPS 2021 • Sebastian Damrich, Fred A. Hamprecht

As a consequence, we show that UMAP does not aim to reproduce its theoretically motivated high-dimensional UMAP similarities.

1 code implementation • 26 Nov 2020 • Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht

Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem.

no code implementations • 10 Sep 2020 • Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht

This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style.

no code implementations • 28 Feb 2020 • Steffen Wolf, Fred A. Hamprecht, Jan Funke

Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly.

no code implementations • 29 Dec 2019 • Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht

Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.

1 code implementation • NeurIPS 2019 • Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht

The seeded Watershed algorithm / minimax semi-supervised learning on a graph computes a minimum spanning forest which connects every pixel / unlabeled node to a seed / labeled node.

1 code implementation • 21 Aug 2019 • Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht

In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings.

1 code implementation • 27 Jun 2019 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

As active learning is a scarce data regime, we bootstrap from a well-known heuristic that filters the bulk of data points on which all heuristics would agree, and learn a policy to warp the top portion of this ranking in the most beneficial way for the character of a specific data distribution.

no code implementations • CVPR 2022 • Alberto Bailoni, Constantin Pape, Nathan Hütsch, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht

We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes.

1 code implementation • CVPR 2019 • Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht

We present an end-to-end learned algorithm for seeded segmentation.

no code implementations • 25 Apr 2019 • Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht

Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.

1 code implementation • ICLR 2019 • Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Jakobi, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht

Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing.

2 code implementations • ICLR 2019 • Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy.

1 code implementation • 19 May 2018 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation.

no code implementations • 15 Mar 2018 • Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht

Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model.

2 code implementations • ICML 2018 • Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht

Training neural networks involves finding minima of a high-dimensional non-convex loss function.

1 code implementation • 7 Dec 2017 • Thomas Hehn, Fred A. Hamprecht

Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data.

1 code implementation • NeurIPS 2017 • Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht

Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing.

1 code implementation • NeurIPS 2017 • Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler

In contrast to previous approaches to learning with cost penalties, our method can grow very deep trees that on average are nonetheless cheap to compute.

no code implementations • CVPR 2018 • Maurice Weiler, Fred A. Hamprecht, Martin Storath

In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input.

Ranked #2 on Breast Tumour Classification on PCam

Breast Tumour Classification
Colorectal Gland Segmentation:
**+2**

1 code implementation • CVPR 2017 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

Gaussian Processes (GPs) are effective Bayesian predictors.

no code implementations • CVPR 2016 • Ferran Diego, Fred A. Hamprecht

We propose a new way to train a structured output prediction model.

1 code implementation • CVPR 2015 • Thorsten Beier, Fred A. Hamprecht, Jorg H. Kappes

Correlation clustering, or multicut partitioning, is widely used in image segmentation for partitioning an undirected graph or image with positive and negative edge weights such that the sum of cut edge weights is minimized.

no code implementations • NeurIPS 2014 • Ferran Diego Andilla, Fred A. Hamprecht

Experiments on real and synthetic data demonstrate the viability of the proposed method.

no code implementations • CVPR 2014 • Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht

We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames.

no code implementations • CVPR 2014 • Thorsten Beier, Thorben Kroeger, Jorg H. Kappes, Ullrich Kothe, Fred A. Hamprecht

Since this problem is NP-hard, we propose a new approximate solver based on the move-making paradigm: first, the graph is recursively partitioned into small regions (cut phase).

no code implementations • 2 Apr 2014 • Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

no code implementations • NeurIPS 2013 • Ferran Diego Andilla, Fred A. Hamprecht

In some applications, however, there is a natural hierarchy of concepts that ought to be reflected in the unsupervised analysis.

no code implementations • NeurIPS 2011 • Xinghua Lou, Fred A. Hamprecht

We study the problem of learning to track a large quantity of homogeneous objects such as cell tracking in cell culture study and developmental biology.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.