scholarly journals An Empirical Study of Bugs in Machine Learning Systems

2012 ◽  
Author(s):  
Ferdian Thung ◽  
Shaowei Wang ◽  
David Lo ◽  
Lingxiao Jiang
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Learning Systems

scholarly journals An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems

2021 ◽  
Author(s):  
Yiming Tang ◽  
Raffi Khatchadourian ◽  
Mehdi Bagherzadeh ◽  
Rhia Singh ◽  
Ajani Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Learning Systems ◽  
Technical Debt

Tensor relational algebra for distributed machine learning system design

2021 ◽  
Vol 14 (8) ◽  
pp. 1338-1350
Author(s):  
Binhang Yuan ◽  
Dimitrije Jankov ◽  
Jia Zou ◽  
Yuxin Tang ◽  
Daniel Bourgeois ◽  
...  
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
System Design ◽  
High Efficiency ◽  
Learning Systems ◽  
Learning System ◽  
Relational Algebra ◽  
Activation Functions ◽  
Distributed Environment ◽  
Distributed Machine Learning

We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system? Current machine learning systems typically push whole tensors through a series of compute kernels such as matrix multiplications or activation functions, where each kernel runs on an AI accelerator (ASIC) such as a GPU. This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC. In this paper, we present an alternative implementation abstraction called the tensor relational algebra (TRA). The TRA is a set-based algebra based on the relational algebra. Expressions in the TRA operate over binary tensor relations, where keys are multi-dimensional arrays and values are tensors. The TRA is easily executed with high efficiency in a parallel or distributed environment, and amenable to automatic optimization. Our empirical study shows that the optimized TRA-based back-end can significantly outperform alternatives for running ML workflows in distributed clusters.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

Cardiovascular Diseases Classification Via Machine Learning Systems

2021 ◽  
Author(s):  
Fadheela Hussain ◽  
Mustafa Hammad ◽  
Wael El-Medany ◽  
Riadh Ksantini
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Learning Systems
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