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Found 6 papers, 2 papers with code
GVFs in the Real World: Making Predictions Online for Water Treatment
In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant.
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned by deep reinforcement learning systems.
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection
Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA).
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning
Soft-greedy operators, namely $\varepsilon$-greedy and softmax, remain a common choice to induce a basic level of exploration for action-value methods in reinforcement learning.
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
Under such settings, existing transfer evolutionary optimization frameworks grapple with simultaneously satisfying two important quality attributes, namely (1) scalability against a growing number of source tasks and (2) online learning agility against sparsity of relevant sources to the target task of interest.
Genetic Neural Architecture Search for automatic assessment of human sperm images
Every individual of the genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome), and its fitness is calculated by a novel proposed method named GeNAS-WF especially designed for noisy, low resolution, and imbalanced datasets.
