Search Results for author: Bjoern Eskofier

Found 14 papers, 5 papers with code

From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations

no code implementations21 May 2023 Toni Albert, Bjoern Eskofier, Dario Zanca

In this paper, we propose a novel auxiliary pretraining method that is based on spatial reasoning.

Contrastive Learning

Simulating Human Gaze with Neural Visual Attention

no code implementations22 Nov 2022 Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

Existing models of human visual attention are generally unable to incorporate direct task guidance and therefore cannot model an intent or goal when exploring a scene.

Active Learning of Ordinal Embeddings: A User Study on Football Data

no code implementations26 Jul 2022 Christoffer Loeffler, Kion Fallah, Stefano Fenu, Dario Zanca, Bjoern Eskofier, Christopher John Rozell, Christopher Mutschler

We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples.

Active Learning Information Retrieval +3

Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

1 code implementation14 Mar 2022 Christoffer Loeffler, Wei-Cheng Lai, Bjoern Eskofier, Dario Zanca, Lukas Schmidt, Christopher Mutschler

Explanatory visual interpretation approaches for image, and natural language processing allow domain experts to validate and understand almost any deep learning model.

Time Series Time Series Analysis +2

Objective Evaluation of Deep Visual Interpretations on Time Series Data

no code implementations29 Sep 2021 Christoffer Löffler, Wei-Cheng Lai, Lukas M Schmidt, Dario Zanca, Bjoern Eskofier, Christopher Mutschler

(Explanatory) visual interpretation approaches for image and natural language processing allow domain experts to validate and understand almost any deep learning model.

Time Series Time Series Analysis +1

Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer

no code implementations8 Jul 2021 Mohamad Wehbi, Tim Hamann, Jens Barth, Bjoern Eskofier

This system is applicable in real-world applications and requires no user-specific training for recognition.

Handwriting Recognition

Towards an IMU-based Pen Online Handwriting Recognizer

no code implementations26 May 2021 Mohamad Wehbi, Tim Hamann, Jens Barth, Peter Kaempf, Dario Zanca, Bjoern Eskofier

Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data.

Handwriting Recognition Language Modelling

Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks

no code implementations21 May 2021 Leo Schwinn, René Raab, An Nguyen, Dario Zanca, Bjoern Eskofier

Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community.

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

1 code implementation24 Feb 2021 Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier

The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications.

System Design for a Data-driven and Explainable Customer Sentiment Monitor

1 code implementation11 Jan 2021 An Nguyen, Stefan Foerstel, Thomas Kittler, Andrey Kurzyukov, Leo Schwinn, Dario Zanca, Tobias Hipp, Da Jun Sun, Michael Schrapp, Eva Rothgang, Bjoern Eskofier

The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices.

Interpretable Machine Learning Management

Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks

no code implementations5 Nov 2020 Leo Schwinn, An Nguyen, René Raab, Dario Zanca, Bjoern Eskofier, Daniel Tenbrinck, Martin Burger

We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces.

Adversarial Attack

Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment

1 code implementation21 Oct 2020 An Nguyen, Wenyu Zhang, Leo Schwinn, Bjoern Eskofier

Process Mining has recently gained popularity in healthcare due to its potential to provide a transparent, objective and data-based view on processes.

Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

1 code implementation2 Oct 2020 An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern Eskofier

To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells.

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