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Found 13 papers, 2 papers with code
Reinforcement Learning with Ensemble Model Predictive Safety Certification
Reinforcement learning algorithms need exploration to learn.
Towards Interpretable Classification of Leukocytes based on Deep Learning
Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency.
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data
However, the learning efficiency strongly relies on the initial model, resulting in the trade-off between the size of the initial dataset and the query number.
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis
As a direction for future research, we suggest a combination of Self-Organizing Maps and feature extraction based on deep learning.
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring
To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data.
Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors
In this work, we propose a data-driven approach to optimize the parameters of a simulation such that control policies can be directly transferred from simulation to a real-world quadrotor.
Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation
First, we conduct a critical point analysis of the error function and provide technical insights on optimisation and design choices for neural networks.
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
In this paper, we examine the utility of extending PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight how they can be a scalable and interpretable option for control systems.
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts
This report first provides a brief overview of a number of supervised learning algorithms for regression tasks.
The Virtuous Machine - Old Ethics for New Technology?
Modern AI and robotic systems are characterized by a high and ever-increasing level of autonomy.
$\ell_1$ Regularized Gradient Temporal-Difference Learning
In this paper, we study the Temporal Difference (TD) learning with linear value function approximation.
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
View synthesis is a process for generating novel views from a scene which has been recorded with a 3-D camera setup.
Analysis Operator Learning and Its Application to Image Reconstruction
Our method is based on an $\ell_p$-norm minimization on the set of full rank matrices with normalized columns.
