Internal process of a policy gradient block. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Training process of a DDPG individual. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

PbGA search phases to find the best individuals. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Previous usages of DRL in solving PDG problems. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Internal process of a critic gradient block. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Best Individuals from the mapping phase. by Larasmoyo Nugroho (18078260)

“…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”

Table_1_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …”

Table_2_Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.DOCX by Nina de Lacy (6559520)

“…In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. …”

datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf by Jan Tönshoff (10192709)

“…We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. …”

The_Code_for_High_Order_Analytical_Continuation by Jian Ma (19747060)

“…The optimal order of the analytical continuation algorithm is contingent upon the noise level of gravity data. …”

Additional information on the sequence model. by Jérôme Tubiana (14559981)

Contextual Dynamic Pricing with Strategic Buyers by Pangpang Liu (18886419)

“…This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. …”

Low-Order Scaling <i>G</i>₀<i>W</i>₀ by Pair Atomic Density Fitting by Arno Förster (8356044)

“…We derive a low-scaling <i>G</i>₀<i>W</i>₀ algorithm for molecules using pair atomic density fitting (PADF) and an imaginary time representation of the Green’s function and describe its implementation in the Slater type orbital (STO)-based Amsterdam density functional (ADF) electronic structure code. …”

Data_Sheet_1_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …”

Data_Sheet_2_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …”

Data_Sheet_3_Multivariate Brain Functional Connectivity Through Regularized Estimators.DOCX by Raymond Salvador (813880)

“…<p>Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<br><br>Methods<br><br>This work is a quantitative and experimental study of supervised classification. …”

Metabolomic Coverage of Chemical-Group-Submetabolome Analysis: Group Classification and Four-Channel Chemical Isotope Labeling LC-MS by Shuang Zhao (484057)

“…We developed a computer algorithm to classify chemical structures according to their functional groups. …”

Metabolomic Coverage of Chemical-Group-Submetabolome Analysis: Group Classification and Four-Channel Chemical Isotope Labeling LC-MS by Shuang Zhao (484057)

“…We developed a computer algorithm to classify chemical structures according to their functional groups. …”