Fairness Testing of Machine Learning Models Using Deep Reinforcement Learning

2020 ◽  
Author(s):  
Wentao Xie ◽  
Peng Wu
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Machine Learning Combinatorial Frameworks for Architecture

2021 ◽  
Vol 7 (2) ◽  
pp. 20-29
Author(s):  
Joshua Lye ◽  
Alisa Andrasek
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Imitation Learning ◽  
Learning Models ◽  
Learning Agent ◽  
Policy Optimization ◽  
Machine Learning Models ◽  
Reinforcement Learning Algorithm

This paper investigates the application of machine learning for the simulation of larger architectural aggregations formed through the recombination of discrete components. This is primarily explored through establishing hardcoded assembly and connection logics which are used to form the framework of architectural fitness conditions for machine learning models. The key machine learning models researched are a combination of the deep reinforcement learning algorithm proximal policy optimization (PPO) and Generative Adversarial Imitation Learning (GAIL) in the Unity Machine Learning Agent asset toolkit. The goal of applying these machine learning models is to train the agent behaviours (discrete components) to learn specific logics of connection. In order to achieve assembled architectural `states that allow for spatial habitation through the process of simulation.

scholarly journals Methods of intelligent protection from asymmetrical conditions in electric networks

2020 ◽  
Vol 209 ◽  
pp. 07004
Author(s):  
H.B Guliyev ◽  
N.V. Tomin ◽  
F.Sh Ibrahimov
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Electrical Network ◽  
Learning Models ◽  
Structural Diagram ◽  
Electric Networks ◽  
Drive Systems ◽  
And Control ◽  
Machine Learning Models

Possible cases of non-fulfilment of the requirements of the necessary sensitivity and the selectivity of the existing protection against incomplete-phase and asymmetric modes in the electrical network under conditions of uncertainty of the initial data are determined. The paper considers the issue of intellectualization of protection from asymmetric modes based on theories of fuzzy logic, as well as machine learning models, and offers a structural diagram and an algorithm for the functioning of protection. The results of the synthesis of intelligent protection and an approach to modelling and control for controlled drive systems based on reinforcement learning are presented.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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