Learning and inference over relational data

Learning and inference over relational data

Relational data is ubiquitous in modern-day computing, and drives several key applications across multiple domains, such as information retrieval, question answering, recommendation systems, and drug discovery. As a result, a main research question in artificial intelligence (AI) is to build models...

Fuld beskrivelse

Bibliografiske detaljer
Hovedforfatter:	Abboud, R
Andre forfattere:	Ceylan, I
Format:	Thesis
Sprog:	English
Udgivet:	2022
Fag:	Deep learning (Machine learning) Artificial intelligence Machine learning Supervised learning (Machine learning)

Lignende værker

The elements of statistical learning : data mining, inference and prediction /
af: 333832 Hastie, T., et al.
Udgivet: (2001)

Influence of the input data on learning deep representations
af: Sylvestre-Alvise Rebuffi
Udgivet: (2020)

Deep learning with hard logical constraints
af: Giunchiglia, E
Udgivet: (2022)

Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects
af: George Obaido, et al.
Udgivet: (2024-09-01)

Practical considerations regarding classification learning for clinical diagnosis and therapy advice in oncology
af: Flavio S. Correa da Silva, et al.
Udgivet: (2020-09-01)

A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study
af: Jae-Hong Lee, et al.
Udgivet: (2020-11-01)

Deep Reinforcement Learning in complex environments
af: Nardelli, N
Udgivet: (2021)

Towards robust neural networks: evaluation and construction
af: Lu, J
Udgivet: (2021)

An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases
af: Andre Wirries, et al.
Udgivet: (2022-03-01)

Introduction to Semi-supervised learning /
af: 352292 Zhu, Xiojin, et al.
Udgivet: (2006)

Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals
af: Dong-Woon Lee, et al.
Udgivet: (2021-02-01)

Advances in Machine Learning (Branch of Artificial Intelligence) /
af: Cooney, Margert author 640107
Udgivet: (2012)

ManufacturingNet: A machine learning toolbox for engineers
af: Akshay Antony, et al.
Udgivet: (2023-07-01)

Quantifying spatial heterogeneity in tumours from dynamic contrast-enhanced MRI
af: Mirecka, J
Udgivet: (2019)

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision
af: Chakor Ahmed Yassine, et al.
Udgivet: (2022-06-01)

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review
af: Celine M. Schreidah, et al.
Udgivet: (2024-04-01)

Recent advances in system management, decision support systems, artificial intelligence and computing in anaesthesia
af: Bhavna Gupta, et al.
Udgivet: (2023-01-01)

Smart Augmentation Learning an Optimal Data Augmentation Strategy
af: Joseph Lemley, et al.
Udgivet: (2017-01-01)

Spontaneous Concept Learning with Deep Autoencoder
af: Serge Dolgikh
Udgivet: (2018-11-01)

Machine learning and deep learning methods that use omics data for metastasis prediction
af: Somayah Albaradei, et al.
Udgivet: (2021-01-01)

Semi-supervised learning in cancer diagnostics
af: Jan-Niklas Eckardt, et al.
Udgivet: (2022-07-01)

Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review
af: Sumayh S. Aljameel, et al.
Udgivet: (2023-02-01)

Adversarial robustness of Bayesian neural networks
af: Wicker, M
Udgivet: (2021)

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis
af: Xim Bokhimi
Udgivet: (2021-10-01)

On the adversarial robustness of Bayesian machine learning models
af: Blaas, AC
Udgivet: (2021)

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community
af: Alessio Bottrighi, et al.
Udgivet: (2023-09-01)

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge
af: Paola Lecca
Udgivet: (2021-09-01)

Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges
af: Ravil I. Mukhamediev, et al.
Udgivet: (2022-07-01)

Applications of Artificial Intelligence (AI) in medicine
af: Kacper Niewęgłowski, et al.
Udgivet: (2021-09-01)

Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: Toward Effective Business Management in the Saudi Arabian Context
af: Wael Basri
Udgivet: (2020-01-01)

The Scope of Artificial Intelligence Applications in Medicine: A Review Article
af: Mohammad-Hadi Foroughmand-Araabi
Udgivet: (2023-07-01)

Learning with partial labels: an exploratory study of data scarcity for medical images
af: Dong, N
Udgivet: (2023)

Corrigendum: Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge
af: Paola Lecca
Udgivet: (2022-04-01)

A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting
af: O’Leary Christian, et al.
Udgivet: (2024-12-01)

Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks
af: Manuel Titos, et al.
Udgivet: (2022-01-01)

Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management
af: Innes Murdo Black, et al.
Udgivet: (2021-11-01)

MACHINE LEARNING : Concepts, Tools and Data Visualization /
af: Kang, Minsoo, author 636774, et al.
Udgivet: ([202)

Artificial Intelligence and Deep Learning for Decision Makers : A Growth Hacker's Guide to Cutting Edge Technologies /
af: Kaur, Jagreet, Dr., author 637178, et al.
Udgivet: (2020)

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis
af: Mirjam Quaak, et al.
Udgivet: (2021-01-01)

Using machine learning to discover candidate localised transcripts from microscopy and genome-wide bioinformatics data
af: Kiourlappou, M
Udgivet: (2023)