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CLIP: Cheap Lipschitz Training of Neural Networks 8th

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CLIP: Cheap Lipschitz Training of Neural Networks

8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021

作者： Bungert, Leon Raab, René Roith, Tim Schwinn, Leo Tenbrinck, Daniel Department Mathematics Friedrich-Alexander University Erlangen-Nürnberg Cauerstraße 11 Erlangen91058 Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Carl-Thiersch-Straße 2b Erlangen91052 Germany

ISBN: (纸本)9783030755485

Despite the large success of deep neural networks (DNN) in recent years, most neural networks still lack mathematical guarantees in terms of stability. For instance, DNNs are vulnerable to small or even imperceptible input perturbations, so called adversarial examples, that can cause false predictions. This instability can have severe consequences in applications which influence the health and safety of humans, e.g., biomedical imaging or autonomous driving. While bounding the Lipschitz constant of a neural network improves stability, most methods rely on restricting the Lipschitz constants of each layer which gives a poor bound for the actual Lipschitz constant. In this paper we investigate a variational regularization method named CLIP for controlling the Lipschitz constant of a neural network, which can easily be integrated into the training procedure. We mathematically analyze the proposed model, in particular discussing the impact of the chosen regularization parameter on the output of the network. Finally, we numerically evaluate our method on both a nonlinear regression problem and the MNIST and Fashion-MNIST classification databases, and compare our results with a weight regularization approach. © 2021, Springer Nature Switzerland AG.

关键词： Stability

Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

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arXiv 2022年

作者： Biggio, Luca Vernardos, Georgios Galan, Aymeric Peel, Austin Data Analytics Lab Institute of Machine Learning Department of Computer Science ETHZ Switzerland Observatoire de Sauverny Versoix1290 Switzerland

Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex mass models than smooth analytical profiles, such as power-law ellipsoids. In this work, we introduce a continuous neural field to predict the lensing potential at any position throughout the image plane, allowing for a nearly model-independent description of the lensing mass. We apply our method on simulated Hubble Space Telescope imaging data containing different types of perturbations to a smooth mass distribution: a localized dark subhalo, a population of subhalos, and an external shear perturbation. Assuming knowledge of the source surface brightness, we use the continuous neural field to model either the perturbations alone or the full lensing potential. In both cases, the resulting model is able to fit the imaging data, and we are able to accurately recover the properties of both the smooth potential and of the perturbations. Unlike many other deep learning methods, ours explicitly retains lensing physics (i.e., the lens equation) and introduces high flexibility in the model only where required, namely, in the lens potential. Moreover, the neural network does not require pre-training on large sets of labelled data and predicts the potential from the single observed lensing image. Our model is implemented in the fully differentiable lens modeling code Herculens. © 2022, CC BY.

关键词： Gravitation

Towards an IMU-based pen online handwriting recognizer

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arXiv 2021年

作者： Wehbi, Mohamad Hamann, Tim Barth, Jens Kaempf, Peter Zanca, Dario Eskofier, Bjoern Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany STABILO International GmbH Germany

Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss that allows the interpretation of raw sensor data into words without the need of sequence segmentation. We use a dataset of words collected using multiple sensor-enhanced pens and evaluate our model on distinct test sets of seen and unseen words achieving a character error rate of 17.97% and 17.08%, respectively, without the use of a dictionary or language model. © 2021, CC BY.

关键词： Statistical tests

machine learning Techniques for Stress Prediction in Working Employees

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Machine Learning Techniques for Stress Prediction in Working...

IEEE International Conference on Computational Intelligence and Computing Research

作者： U Srinivasulu Reddy Aditya Vivek Thota A Dharun Department of Computer Applications Machine Learning and Data Analytics Lab National Institute of Technology Trichy ECE National Institute of Technology Trichy

ISBN: (纸本)9781538615096

Stress disorders are a common issue among working IT professionals in the industry today. With changing lifestyle and work cultures, there is an increase in the risk of stress among the employees. Though many industries and corporates provide mental health related schemes and try to ease the workplace atmosphere, the issue is far from control. In this paper, we would like to apply machine learning techniques to analyze stress patterns in working adults and to narrow down the factors that strongly determine the stress levels. Towards this, data from the OSMI mental health survey 2017 responses of working professionals within the tech-industry was considered. Various machine learning techniques were applied to train our model after due data cleaning and preprocessing. The accuracy of the above models was obtained and studied comparatively. Boosting had the highest accuracy among the models implemented. By using Decision Trees, prominent features that influence stress were identified as gender, family history and availability of health benefits in the workplace. With these results, industries can now narrow down their approach to reduce stress and create a much comfortable workplace for their employees.

关键词： data handling decision trees DP management employee welfare ergonomics gender issues health care learning (artificial intelligence) occupational stress organisational aspects personnel psychology risk analysis computer installation management Decision Trees Organizational aspects gender issue data processing stress at work Ergonomics Psychology Risk Management risk analysis Personnel Employees Health Care data cleaning learning methods

IALE: imitating active learner ensembles

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The Journal of Machine Learning Research

The Journal of machine learning Research 2022年第1期23卷 4632-4660页

作者： Christoffer Löffler Christopher Mutschler Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany

Active learning prioritizes the labeling of the most informative data samples. However, the performance of active learning heuristics depends on both the structure of the underlying model architecture and the data. We propose IALE, an imitation learning scheme that imitates the selection of the best-performing expert heuristic at each stage of the learning cycle in a batch-mode pool-based setting. We use DAGGER to train a transferable policy on a dataset and later apply it to different datasets and deep classifier architectures. The policy reects on the best choices from multiple expert heuristics given the current state of the active learning process, and learns to select samples in a complementary way that unifies the expert strategies. Our experiments on well-known image datasets show that we outperform state of the art imitation learners and heuristics.

关键词： active learning deep neural networks imitation learning dataset aggregation transferable policy

Automatic clinical gait test detection from inertial sensor data

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Automatic clinical gait test detection from inertial sensor ...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stefan Fischer Martin Ullrich Arne Küderle Heiko Gaßner Jochen Klucken Bjoern M. Eskofier Felix Kluge Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Germany University Hospital Erlangen Germany

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.

关键词： Turning Diseases Instruments Legged locomotion Gyroscopes Hospitals Reliability

A.I. and data-Driven Mobility at Volkswagen Financial Services AG

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arXiv 2022年

作者： Jawed, Shayan Arif, Mofassir Ul Islam Rashed, Ahmed Madhusudhanan, Kiran Elsayed, Shereen Jameel, Mohsan Schmidt-Thieme, Lars Volk, Alexei Hintsches, Andre Kornfeld, Marlies Lange, Katrin Information Systems and Machine Learning Lab University of Hildesheim Germany Data Analytics & A.I Unit Volkswagen Financial Services AG Germany

machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aims to leverage existing proprietary data and the latest research to enhance existing and derive new business processes. The collaboration between Information Systems and machine learning lab (ISMLL) and VWFS serves to realize this goal. In this paper, we propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS. © 2022, CC BY-NC-ND.

关键词： machine learning

Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

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TechRxiv

TechRxiv 2023年

作者： Küderle, Arne Ullrich, Martin Roth, Nils Ollenschläger, Malte Ibrahim, Alzhraa Moradi, Hamid Richer, Robert Seifer, Ann-Kristin Zürl, Matthias Sîmpetru, Raul Herzer, Liv Prossel, Dominik Kluge, Felix Eskofier, Bjoern Friedrich-Alexander-Universität Erlangen-Nürnberg Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Germany

Digitizing handwriting with a sensor pen: A writer-independent recognizer

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arXiv 2021年

作者： Wehbi, Mohamad Hamann, Tim Barth, Jens Eskofier, Bjoern Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany STABILO International GmbH Heroldsberg Germany

Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper. © 2021, CC BY.

关键词： Character recognition