Introduction: A Sampler in Knowledge Acquisition for the Machine Learning Community

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Theory and Practice of Logic Programming ◽

10.1017/s1471068418000248 ◽

2018 ◽

Vol 18 (3-4) ◽

pp. 623-637 ◽

Cited By ~ 2

Author(s):

ARINDAM MITRA ◽

CHITTA BARAL

Keyword(s):

Machine Learning ◽

Learning Community ◽

Question Answering ◽

Learning Algorithms ◽

Inductive Logic ◽

Opportunity To Learn ◽

Machine Learning Algorithms ◽

Knowledge Representation And Reasoning ◽

Handwritten Digit ◽

Answer Set

AbstractOver the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available athttps://goo.gl/KdWAcV.

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Emotion Recognition on E-Learning Community to Improve the Learning Outcomes Using Machine Learning Concepts: A Pilot Study

Smart Systems and IoT: Innovations in Computing - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-13-8406-6_50 ◽

2019 ◽

pp. 521-530

Author(s):

A. Jithendran ◽

P. Pranav Karthik ◽

S. Santhosh ◽

J. Naren

Keyword(s):

Machine Learning ◽

Pilot Study ◽

Emotion Recognition ◽

Learning Community ◽

Learning Outcomes ◽

E Learning

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Selected Topics in Artificial Intelligence for Planning and Scheduling Problems, Knowledge Acquisition, and Machine Learning

Batch Processing Systems Engineering ◽

10.1007/978-3-642-60972-5_25 ◽

1996 ◽

pp. 595-630

Author(s):

Aydin K. Sunol ◽

Muzaffer Kapanoglu ◽

Praveen K. Mogili

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Knowledge Acquisition ◽

Scheduling Problems ◽

Planning And Scheduling

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Machine Learning as a Knowledge Acquisition Tool Application in the Domain of the Interpretation of Test Results

AIME 89 - Lecture Notes in Medical Informatics ◽

10.1007/978-3-642-93437-7_4 ◽

1989 ◽

pp. 37-44

Author(s):

R. A. J. Schijven ◽

J. L. Talmon ◽

E. Ermers ◽

R. Penders ◽

P. J. E. H. M. Kitslaar

Keyword(s):

Machine Learning ◽

Knowledge Acquisition ◽

Test Results

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Integrating Machine Learning with Knowledge Acquisition

Expert Systems ◽

10.1016/b978-012443880-4/50070-3 ◽

2002 ◽

pp. 937-959 ◽

Cited By ~ 1

Author(s):

G WEBB

Keyword(s):

Machine Learning ◽

Knowledge Acquisition

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UP-Fall Detection Dataset: A Multimodal Approach

Sensors ◽

10.3390/s19091988 ◽

2019 ◽

Vol 19 (9) ◽

pp. 1988 ◽

Cited By ~ 33

Author(s):

Lourdes Martínez-Villaseñor ◽

Hiram Ponce ◽

Jorge Brieva ◽

Ernesto Moya-Albor ◽

José Núñez-Martínez ◽

...

Keyword(s):

Machine Learning ◽

Learning Community ◽

Wearable Sensors ◽

Fall Detection ◽

Machine Learning Techniques ◽

Elderly Persons ◽

Negative Consequences ◽

Detection Systems ◽

Important Health ◽

Experimental Use

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

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