• Corpus ID: 67750460

Democratisation of Usable Machine Learning in Computer Vision

@article{Bond2019DemocratisationOU,
  title={Democratisation of Usable Machine Learning in Computer Vision},
  author={Raymond R. Bond and Ansgar R. Koene and Alan John Dix and Jennifer Boger and Maurice D. Mulvenna and Mykola Galushka and Brendon Bradley and Fiona Browne and Hui Wang and Alexander Wong},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.06804}
}
Many industries are now investing heavily in data science and automation to replace manual tasks and/or to help with decision making, especially in the realm of leveraging computer vision to automate many monitoring, inspection, and surveillance tasks. This has resulted in the emergence of the 'data scientist' who is conversant in statistical thinking, machine learning (ML), computer vision, and computer programming. However, as ML becomes more accessible to the general public and more aspects… 

Figures from this paper

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

This review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements, and develops a conceptual framework to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system.

Conception of a Reference Architecture for Machine Learning in the Process Industry

This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures and an exemplary implementation in the brewing industry.

“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

This article identifies four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance.

Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

Ethical issues in making use of digital phenotype data in the arena of digital health interventions and the broader issues in democratizing machine learning and artificial intelligence for digital phenotyping data are explored in detail.

References

SHOWING 1-10 OF 20 REFERENCES

Towards Interactive Curation & Automatic Tuning of ML Pipelines

The first iteration of QuIC-M (pronounced quick-m), an interactive human-in-the-loop data exploration and model building suite to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions.

Infrastructure for Usable Machine Learning: The Stanford DAWN Project

This document outlines opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.

Spreadsheet interfaces for usable machine learning

  • Advait Sarkar
  • Computer Science
    2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
  • 2015
A line of research is presented into using the spreadsheet - already familiar to end-users as a paradigm for data manipulation - as a usable interface which lowers the statistical and computing knowledge barriers to building and using these models.

Learning Transferable Architectures for Scalable Image Recognition

This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models.

MnasNet: Platform-Aware Neural Architecture Search for Mobile

An automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.

Weka: Practical machine learning tools and techniques with Java implementations

The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely

RapidMiner: Data Mining Use Cases and Business Analytics Applications

RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors.

FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis

Experimental results for image classification, semantic segmentation, and object detection tasks illustrate the efficacy of generative synthesis in producing generators that automatically generate highly efficient deep neural networks with higher model efficiency and lower computational costs.

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining

The aim of this tutorial is to survey algorithmic bias, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions.

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

CLass-Enhanced Attentive Response can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs.