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09/13/2023
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
Computed tomography (CT) has become an essential part of modern science ...
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08/30/2023
Cyclophobic Reinforcement Learning
In environments with sparse rewards, finding a good inductive bias for e...
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08/22/2023
A Survey on Self-Supervised Representation Learning
Learning meaningful representations is at the heart of many tasks in the...
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03/13/2023
Transformer-based World Models Are Happy With 100k Interactions
Deep neural networks have been successful in many reinforcement learning...
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01/13/2023
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm
Very large state spaces with a sparse reward signal are difficult to exp...
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10/26/2022
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Many production processes are characterized by numerous and complex caus...
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10/01/2022
Blindly Deconvolving Super-noisy Blurry Image Sequences
Image blur and image noise are imaging artifacts intrinsically arising i...
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05/31/2022
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Phase retrieval is the problem of reconstructing images from magnitude-o...
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05/30/2022
Deblurring Photographs of Characters Using Deep Neural Networks
In this paper, we present our approach for the Helsinki Deblur Challenge...
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10/26/2021
A Closer Look at Reference Learning for Fourier Phase Retrieval
Reconstructing images from their Fourier magnitude measurements is a pro...
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06/18/2021
Non-Iterative Phase Retrieval With Cascaded Neural Networks
Fourier phase retrieval is the problem of reconstructing a signal given ...
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06/18/2021
Learning to Plan via a Multi-Step Policy Regression Method
We propose a new approach to increase inference performance in environme...
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04/07/2021
Contour Proposal Networks for Biomedical Instance Segmentation
We present a conceptually simple framework for object instance segmentat...
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03/09/2021
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks
Cytoarchitecture describes the spatial organization of neuronal cells in...
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11/25/2020
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
Cytoarchitectonic maps provide microstructural reference parcellations o...
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11/25/2020
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale
Human brain atlases provide spatial reference systems for data character...
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10/12/2020
Discrete Latent Space World Models for Reinforcement Learning
Sample efficiency remains a fundamental issue of reinforcement learning....
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06/26/2020
Fast Multi-Level Foreground Estimation
Alpha matting aims to estimate the translucency of an object in a given ...
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03/25/2020
PyMatting: A Python Library for Alpha Matting
An important step of many image editing tasks is to extract specific obj...
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12/10/2019
Phase Retrieval using Conditional Generative Adversarial Networks
In this paper, we propose the application of conditional generative adve...
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06/09/2019
On the Vulnerability of Capsule Networks to Adversarial Attacks
This paper extensively evaluates the vulnerability of capsule networks t...
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06/13/2018
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
Cytoarchitectonic parcellations of the human brain serve as anatomical r...
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05/30/2017
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Microscopic analysis of histological sections is considered the "gold st...
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06/28/2014
Learning to Deblur
We describe a learning-based approach to blind image deconvolution. It u...
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03/01/2013
On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit
We establish a link between Fourier optics and a recent construction fro...
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11/07/2012
Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms
Image denoising can be described as the problem of mapping from a noisy ...
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11/07/2012
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds
Image denoising can be described as the problem of mapping from a noisy ...
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12/06/2009
