Tobias Glasmachers

research

∙ 08/01/2023

Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes

To gain a deeper understanding of the behavior and learning dynamics of ...

0 Stephan Johann Lehmler, et al. ∙

research

∙ 07/06/2023

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation

We present ContainerGym, a benchmark for reinforcement learning inspired...

0 Abhijeet Pendyala, et al. ∙

research

∙ 12/29/2022

Invariance to Quantile Selection in Distributional Continuous Control

In recent years distributional reinforcement learning has produced many ...

0 Felix Grün, et al. ∙

research

∙ 07/03/2022

Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!

Support vector machines (SVMs) are a standard method in the machine lear...

0 Tobias Glasmachers, et al. ∙

research

∙ 06/21/2022

ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces

Objective: Electroencephalography (EEG) and electromyography (EMG) are t...

0 Omair Ali, et al. ∙

research

∙ 01/27/2022

From Motion to Muscle

Voluntary human motion is the product of muscle activity that results fr...

0 Marie Dominique Schmidt, et al. ∙

research

∙ 12/30/2021

Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification

Accurate decoding of surface electromyography (sEMG) is pivotal for musc...

13 Stephan Johann Lehmler, et al. ∙

research

∙ 12/01/2021

The (1+1)-ES Reliably Overcomes Saddle Points

It is known that step size adaptive evolution strategies (ES) do not con...

0 Tobias Glasmachers, et al. ∙

research

∙ 11/30/2020

Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation

Brain-computer interfaces (BCIs) enable direct communication between hum...

4 Omair Ali, et al. ∙

research

∙ 11/11/2020

Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation

In stochastic optimization, particularly in evolutionary computation and...

0 Nils Müller, et al. ∙

research

∙ 09/20/2020

Latent Representation Prediction Networks

Deeply-learned planning methods are often based on learning representati...

0 Hlynur Davíð Hlynsson, et al. ∙

research

∙ 09/18/2020

Global Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions

Evolution strategies (ESs) are zero-order stochastic black-box optimizat...

0 Youhei Akimoto, et al. ∙

research

∙ 09/14/2020

Methods of the Vehicle Re-identification

Most of researchers use the vehicle re-identification based on classific...

0 Mohamed Nafzi, et al. ∙

research

∙ 09/14/2020

Data Augmentation and Clustering for Vehicle Make/Model Classification

Vehicle shape information is very important in Intelligent Traffic Syste...

0 Mohamed Nafzi, et al. ∙

research

∙ 09/06/2020

Convergence Analysis of the Hessian Estimation Evolution Strategy

The class of algorithms called Hessian Estimation Evolution Strategies (...

0 Tobias Glasmachers, et al. ∙

research

∙ 04/16/2020

Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing

We propose a novel method for analyzing and visualizing the complexity o...

80 Declan Oller, et al. ∙

research

∙ 03/30/2020

The Hessian Estimation Evolution Strategy

We present a novel black box optimization algorithm called Hessian Estim...

0 Tobias Glasmachers, et al. ∙

research

∙ 05/15/2019

Vehicle Shape and Color Classification Using Convolutional Neural Network

This paper presents a module of vehicle reidentification based on make/m...

0 Mohamed Nafzi, et al. ∙

research

∙ 10/23/2018

Challenges of Convex Quadratic Bi-objective Benchmark Problems

Convex quadratic objective functions are an important base case in state...

0 Tobias Glasmachers, et al. ∙

research

∙ 06/26/2018

Dual SVM Training on a Budget

We present a dual subspace ascent algorithm for support vector machine t...

0 Sahar Qaadan, et al. ∙

research

∙ 06/26/2018

Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search

Limiting the model size of a kernel support vector machine to a pre-defi...

0 Tobias Glasmachers, et al. ∙

research

∙ 06/26/2018

Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training

Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art techni...

0 Sahar Qaadan, et al. ∙

research

∙ 06/04/2018

Challenges in High-dimensional Reinforcement Learning with Evolution Strategies

Evolution Strategies (ESs) have recently become popular for training dee...

0 Nils Müller, et al. ∙

research

∙ 02/09/2018

Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule

This paper explores the use of the standard approach for proving runtime...

0 Youhei Akimoto, et al. ∙

research

∙ 06/09/2017

Global Convergence of the (1+1) Evolution Strategy

We establish global convergence of the (1+1)-ES algorithm, i.e., converg...

0 Tobias Glasmachers, et al. ∙

research

∙ 05/18/2017

Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popula...

0 Ilya Loshchilov, et al. ∙

research

∙ 04/26/2017

Limits of End-to-End Learning

End-to-end learning refers to training a possibly complex learning syste...

0 Tobias Glasmachers, et al. ∙

research

∙ 05/09/2016

Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)

We propose a multi-objective optimization algorithm aimed at achieving g...

0 Ilya Loshchilov, et al. ∙

research

∙ 02/10/2016

Fast model selection by limiting SVM training times

Kernelized Support Vector Machines (SVMs) are among the best performing ...

0 Aydin Demircioglu, et al. ∙

research

∙ 01/15/2014

Coordinate Descent with Online Adaptation of Coordinate Frequencies

Coordinate descent (CD) algorithms have become the method of choice for ...

0 Tobias Glasmachers, et al. ∙

research

∙ 05/02/2013

Testing Hypotheses by Regularized Maximum Mean Discrepancy

Do two data samples come from different distributions? Recent studies of...

0 Somayeh Danafar, et al. ∙

research

∙ 02/22/2013

Accelerated Linear SVM Training with Adaptive Variable Selection Frequencies

Support vector machine (SVM) training is an active research area since t...

0 Tobias Glasmachers, et al. ∙

research

∙ 06/22/2011

Natural Evolution Strategies

This paper presents Natural Evolution Strategies (NES), a recent family ...

0 Daan Wierstra, et al. ∙

Tobias Glasmachers

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