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Perceptrons Under Verifiable Random Data Corruption
We study perceptrons when datasets are randomly corrupted by noise and subsequently such corrupted examples are discarded from the training process....
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ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
We present ContainerGym, a benchmark for reinforcement learning inspired by a real-world industrial resource allocation task. The proposed benchmark...
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Learning Graph Configuration Spaces with Graph Embedding in Engineering Domains
In various domains, engineers face the challenge of optimising system configurations while considering numerous constraints. A common goal is not to...
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Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison
Real-world reinforcement learning (RL) environments, whether in robotics or industrial settings, often involve non-visual observations and require...
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Multi-scale Heat Kernel Graph Network for Graph Classification
Graph neural networks (GNNs) have been shown to be useful in a variety of graph classification tasks, from bioinformatics to social networks....
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Reinforcement Learning for Multi-Neighborhood Local Search in Combinatorial Optimization
This study investigates the application of reinforcement learning for the adaptive tuning of neighborhood probabilities in stochastic...
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LSTM Noise Robustness: A Case Study for Heavy Vehicles
Artificial intelligence (AI) techniques are becoming more and more widespread. This is directly related to technology progress and aspects as the...
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Ensemble Clustering for Boundary Detection in High-Dimensional Data
The emergence of novel data collection methods has led to the accumulation of vast amounts of unlabelled data. Discovering well separated groups of...
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A Radically New Theory of How the Brain Represents and Computes with Probabilities
It is widely believed that the brain implements probabilistic reasoning and that it represents information via some form of population (distributed)...
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Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning
Ökolopoly is a serious game developed by biochemist Frederic Vester with the goal to enhance understanding of interactions in complex systems. Due to...
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Attentive Perturbation: Extending Prefix Tuning to Large Language Models Inner Representations
From adapters to prefix-tuning, parameter efficient fine-tuning (PEFT) has been a well investigated research field in the past few years, which has...
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ProVolOne – Protein Volume Prediction Using a Multi-attention, Multi-resolution Deep Neural Network and Finite Element Analysis
Protein structural properties are often determined by experimental techniques such as X-ray crystallography and nuclear magnetic resonance. However,...
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SoftCut: A Fully Differentiable Relaxed Graph Cut Approach for Deep Learning Image Segmentation
Graph cut algorithms can produce consistent high-quality image segmentation masks by minimizing a predefined energy function over pixels. However,...
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Bilevel Optimization by Conditional Bayesian Optimization
Bilevel optimization problems have two decision-makers: a leader and a follower (sometimes more than one of either, or both). The leader must solve a...
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Dynamic Soaring in Uncertain Wind Conditions: Polynomial Chaos Expansion Approach
Dynamic soaring refers to a flight technique used primarily by large seabirds to extract energy from the wind shear layers formed above ocean...
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Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks
Logic-based Benders decomposition is a technique to solve optimization problems to optimality. It works by splitting the problem into a master...
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Sensitivity Analysis for Feature Importance in Predicting Alzheimer’s Disease
Artificial Intelligence (AI) classifier models based on Deep Neural Networks (DNN) have demonstrated superior performance in medical diagnostics....
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Hybrid Model for Impact Analysis of Climate Change on Droughts in Indian Region
Droughts are prolonged periods of dry weather that have become more frequent and severe due to climate change and global warming. It can have...
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Consensus-Based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning
The legitimacy of bottom-up democratic processes for the distribution of public funds by policy-makers is challenging and complex. Participatory...
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On Ensemble Learning for Mental Workload Classification
The ability to determine a subject’s Mental Work Load (MWL) has a wide range of significant applications within modern working environments. In...