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Found 19 papers, 3 papers with code
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
Motion prediction is an important aspect for Autonomous Driving (AD) and Advance Driver Assistance Systems (ADAS).
Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades.
Improved Single Camera BEV Perception Using Multi-Camera Training
This raises the problem of developing a BEV perception model that provides a sufficient performance on a low-cost sensor setup.
A Survey on Emergent Language
Its objective is to serve as a reference for researchers interested in or proficient in the field.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Task Weighting through Gradient Projection for Multitask Learning
This is commonly addressed by using the Gradient Projection algorithm PCGrad that often leads to faster convergence and improved performance metrics.
Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings
The rapid evolution of technology has transformed business operations and customer interactions worldwide, with personalization emerging as a key opportunity for e-commerce companies to engage customers more effectively.
Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation
This technical report outlines our method for generating a synthetic dataset for semantic segmentation using a latent diffusion model.
Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
In addition, we propose an algorithm for obtaining the optimal parameterization for such a given number of solutions and any given trained agent.
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes
The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance.
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
In this paper, we further improve DLR as an underlying method by actively incorporating the variability of difficulty within the same problem size into the design of the learning process.
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings.
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents.
Towards NLP-supported Semantic Data Management
Finally, we will use the created ontology and automatically identified semantic models to either rate descriptions for new data sources or even to automatically generate descriptive texts that are easier to understand by the human user than formal models.
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
We show that the healthy agent's behavior is characterized by a distinct correlation pattern between the network's layer activation and the performed actions during an episode and that network ablations, which cause a strong change of this pattern, lead to the agent failing its trained control task.
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics.
Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking.
Ablation Studies in Artificial Neural Networks
considering the growth in size and complexity of state-of-the-art artificial neural networks (ANNs) and the corresponding growth in complexity of the tasks that are tackled by these networks, the question arises whether ablation studies may be used to investigate these networks for a similar organization of their inner representations.
Ablation of a Robot's Brain: Neural Networks Under a Knife
It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward.
