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  1. Book ; Online: Recipe for Fast Large-scale SVM Training

    Glasmachers, Tobias

    Polishing, Parallelism, and more RAM!

    2022  

    Abstract: Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. That problem is aggravated ...

    Abstract Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. That problem is aggravated by the exponential growth of data volumes over time. It was tackled in the past mainly by two types of techniques: approximate solvers, and parallel GPU implementations. In this work, we combine both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24 minutes.
    Keywords Computer Science - Machine Learning
    Publishing date 2022-07-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Global Convergence of the (1 + 1) Evolution Strategy to a Critical Point.

    Glasmachers, Tobias

    Evolutionary computation

    2019  Volume 28, Issue 1, Page(s) 27–53

    Abstract: We establish global convergence of the (1 + 1) evolution strategy, that is, convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a ... ...

    Abstract We establish global convergence of the (1 + 1) evolution strategy, that is, convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a critical point to measurable functions. At its core, the analysis is based on a novel progress guarantee for elitist, rank-based evolutionary algorithms. By applying it to the (1 + 1) evolution strategy we are able to provide an accurate characterization of whether global convergence is guaranteed with full probability, or whether premature convergence is possible. We illustrate our results on a number of example applications ranging from smooth (non-convex) cases over different types of saddle points and ridge functions to discontinuous and extremely rugged problems.
    MeSH term(s) Algorithms ; Biological Evolution ; Computer Simulation ; Models, Statistical ; Neural Networks, Computer
    Language English
    Publishing date 2019-01-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2022147-2
    ISSN 1530-9304 ; 1063-6560
    ISSN (online) 1530-9304
    ISSN 1063-6560
    DOI 10.1162/evco_a_00248
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

    Maus, Tom / Zengeler, Nico / Sänger, Dorothee / Glasmachers, Tobias

    Sensors (Basel, Switzerland)

    2024  Volume 24, Issue 7

    Abstract: In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their ... ...

    Abstract In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.
    Language English
    Publishing date 2024-03-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s24072114
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: The (1+1)-ES Reliably Overcomes Saddle Points

    Glasmachers, Tobias

    2021  

    Abstract: It is known that step size adaptive evolution strategies (ES) do not converge (prematurely) to regular points of continuously differentiable objective functions. Among critical points, convergence to minima is desired, and convergence to maxima is easy ... ...

    Abstract It is known that step size adaptive evolution strategies (ES) do not converge (prematurely) to regular points of continuously differentiable objective functions. Among critical points, convergence to minima is desired, and convergence to maxima is easy to exclude. However, surprisingly little is known on whether ES can get stuck at a saddle point. In this work we establish that even the simple (1+1)-ES reliably overcomes most saddle points under quite mild regularity conditions. Our analysis is based on drift with tail bounds. It is non-standard in that we do not even aim to estimate hitting times based on drift. Rather, in our case it suffices to show that the relevant time is finite with full probability.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2021-12-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: The concepts of muscle activity generation driven by upper limb kinematics.

    Schmidt, Marie D / Glasmachers, Tobias / Iossifidis, Ioannis

    Biomedical engineering online

    2023  Volume 22, Issue 1, Page(s) 63

    Abstract: Background: The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end- ... ...

    Abstract Background: The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.
    Methods: We develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R
    Results: The presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject.
    Conclusions: The ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes.
    MeSH term(s) Humans ; Male ; Young Adult ; Adult ; Biomechanical Phenomena ; Upper Extremity ; Neural Networks, Computer ; Movement/physiology ; Muscles ; Electromyography/methods
    Language English
    Publishing date 2023-06-24
    Publishing country England
    Document type Journal Article
    ZDB-ID 2084374-4
    ISSN 1475-925X ; 1475-925X
    ISSN (online) 1475-925X
    ISSN 1475-925X
    DOI 10.1186/s12938-023-01116-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation

    Hakenes, Simon / Glasmachers, Tobias

    2023  

    Abstract: This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to object-oriented macro actions, enabling a simple Deep Q-Network (DQN) agent ...

    Abstract This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to object-oriented macro actions, enabling a simple Deep Q-Network (DQN) agent to solve otherwise practically impossible environments.

    Comment: Extended Abstract, Northern Lights Deep Learning Conference 2024, 3 pages, 2 figures
    Keywords Computer Science - Machine Learning
    Publishing date 2023-10-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Convergence Analysis of the Hessian Estimation Evolution Strategy.

    Glasmachers, Tobias / Krause, Oswin

    Evolutionary computation

    2021  Volume 30, Issue 1, Page(s) 27–50

    Abstract: The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested ...

    Abstract The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the (1 + 4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance.
    MeSH term(s) Algorithms ; Biological Evolution
    Language English
    Publishing date 2021-12-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2022147-2
    ISSN 1530-9304 ; 1063-6560
    ISSN (online) 1530-9304
    ISSN 1063-6560
    DOI 10.1162/evco_a_00295
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: tachAId-An interactive tool supporting the design of human-centered AI solutions.

    Bauroth, Max / Rath-Manakidis, Pavlos / Langholf, Valentin / Wiskott, Laurenz / Glasmachers, Tobias

    Frontiers in artificial intelligence

    2024  Volume 7, Page(s) 1354114

    Abstract: In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present " ... ...

    Abstract In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present "technical assistance concerning human-centered AI development" (tachAId), an interactive advisory tool which comprehensively guides AI developers and decision makers in navigating the machine learning lifecycle with a focus on human-centered design. tachAId motivates and presents concrete technical advice to ensure human-centeredness across the phases of AI development. The tool's effectiveness is evaluated through a catalog of criteria for human-centered AI in the form of relevant challenges and goals, derived from existing methodologies and guidelines. Lastly, tachAId and one other comparable advisory tool were examined to determine their adherence to these criteria in order to provide an overview of the human-centered aspects covered by these tools and to allow interested parties to quickly assess whether the tools meet their needs.
    Language English
    Publishing date 2024-03-12
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2024.1354114
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Challenges of Convex Quadratic Bi-objective Benchmark Problems

    Glasmachers, Tobias

    2018  

    Abstract: Convex quadratic objective functions are an important base case in state-of-the-art benchmark collections for single-objective optimization on continuous domains. Although often considered rather simple, they represent the highly relevant challenges of ... ...

    Abstract Convex quadratic objective functions are an important base case in state-of-the-art benchmark collections for single-objective optimization on continuous domains. Although often considered rather simple, they represent the highly relevant challenges of non-separability and ill-conditioning. In the multi-objective case, quadratic benchmark problems are under-represented. In this paper we analyze the specific challenges that can be posed by quadratic functions in the bi-objective case. Our construction yields a full factorial design of 54 different problem classes. We perform experiments with well-established algorithms to demonstrate the insights that can be supported by this function class. We find huge performance differences, which can be clearly attributed to two root causes: non-separability and alignment of the Pareto set with the coordinate system.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2018-10-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Global Convergence of the (1+1) Evolution Strategy

    Glasmachers, Tobias

    2017  

    Abstract: We establish global convergence of the (1+1) evolution strategy, i.e., convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a ... ...

    Abstract We establish global convergence of the (1+1) evolution strategy, i.e., convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a critical point to measurable functions. At its core, the analysis is based on a novel progress guarantee for elitist, rank-based evolutionary algorithms. By applying it to the (1+1) evolution strategy we are able to provide an accurate characterization of whether global convergence is guaranteed with full probability, or whether premature convergence is possible. We illustrate our results on a number of example applications ranging from smooth (non-convex) cases over different types of saddle points and ridge functions to discontinuous and extremely rugged problems.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2017-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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