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Found 44 papers, 6 papers with code
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
In this work, we augment reinforcement learning with an inference-time collision model to ensure safe and efficient container management in a waste-sorting facility with limited processing capacity.
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces.
Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization
We design a class of variable metric evolution strategies well suited for high-dimensional problems.
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders.
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals
By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility.
ProtoP-OD: Explainable Object Detection with Prototypical Parts
Interpretation and visualization of the behavior of detection transformers tends to highlight the locations in the image that the model attends to, but it provides limited insight into the \emph{semantics} that the model is focusing on.
Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System
We introduce a systematic dataset of 3D center-out task-space trajectories of human hand transport movements in a natural setting.
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL).
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes
Our approach models activation patterns of thresholded nodes in (deep) artificial neural networks as stochastic processes.
ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
It is sufficiently versatile to evaluate reinforcement learning algorithms on any real-world problem that fits our resource allocation framework.
Invariance to Quantile Selection in Distributional Continuous Control
In recent years distributional reinforcement learning has produced many state of the art results.
Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!
Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data.
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms.
From Motion to Muscle
Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas.
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on a new subjects data, and compare their performance to subject-specific models.
The (1+1)-ES Reliably Overcomes Saddle Points
It is non-standard in that we do not even aim to estimate hitting times based on drift.
DiBB: Distributing Black-Box Optimization
We present a novel framework for Distributing Black-Box Optimization (DiBB).
Anchored-STFT and GNAA: An extension of STFT in conjunction with an adversarial data augmentation technique for the decoding of neural signals
GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier.
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation
In stochastic optimization, particularly in evolutionary computation and reinforcement learning, the optimization of a function $f: \Omega \to \mathbb{R}$ is often addressed through optimizing a so-called relaxation $\theta \in \Theta \mapsto \mathbb{E}_\theta(f)$ of $f$, where $\Theta$ resembles the parameters of a family of probability measures on $\Omega$.
Latent Representation Prediction Networks
The prediction function is used as a forward model for search on a graph in a viewpoint-matching task and the representation learned to maximize predictability is found to outperform a pre-trained representation.
Data Augmentation and Clustering for Vehicle Make/Model Classification
Vehicle shape information is very important in Intelligent Traffic Systems (ITS).
Methods of the Vehicle Re-identification
It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification.
Convergence Analysis of the Hessian Estimation Evolution Strategy
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.
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
We propose a novel method for analyzing and visualizing the complexity of standard reinforcement learning (RL) benchmarks based on score distributions.
The Hessian Estimation Evolution Strategy
We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed.
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm
We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms.
Vehicle Shape and Color Classification Using Convolutional Neural Network
In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set.
Challenges of Convex Quadratic Bi-objective Benchmark Problems
In this paper we analyze the specific challenges that can be posed by quadratic functions in the bi-objective case.
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-defined budget is a well-established technique that allows to scale SVM learning and prediction to large-scale data.
Dual SVM Training on a Budget
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors.
Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training
Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines.
Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
Our results give insights into which algorithmic mechanisms of modern ES are of value for the class of problems at hand, and they reveal principled limitations of the approach.
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 bounds in discrete domains---often referred to as drift analysis---in the context of optimization on a continuous domain.
Global Convergence of the (1+1) Evolution Strategy
We establish global convergence of the (1+1) evolution strategy, i. e., convergence to a critical point independent of the initial state.
Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular method to deal with nonconvex and/or stochastic optimization problems when the gradient information is not available.
Limits of End-to-End Learning
End-to-end learning system is specifically designed so that all modules are differentiable.
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
We propose a multi-objective optimization algorithm aimed at achieving good anytime performance over a wide range of problems.
Fast model selection by limiting SVM training times
Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods.
Coordinate Descent with Online Adaptation of Coordinate Frequencies
Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning.
The Planning-ahead SMO Algorithm
The sequential minimal optimization (SMO) algorithm and variants thereof are the de facto standard method for solving large quadratic programs for support vector machine (SVM) training.
Testing Hypotheses by Regularized Maximum Mean Discrepancy
Do two data samples come from different distributions?
Natural Evolution Strategies
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutionary algorithms.
Universal Consistency of Multi-Class Support Vector Classification
Steinwart was the first to prove universal consistency of support vector machine classification.
