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A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Daniel Sonntag, Michael Barz, Thiago Gouvêa

Machine Learning · Submitted 27 Jun 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models.

how it is stated · asserted 0.60 read the record →

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arXiv ID2406.19054 (version 1)
Pith IDRQPWXH5F, Pith's permanent record of this paper and its review
CitationDaniel Sonntag, Michael Barz, Thiago Gouv\^ea. "A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)." arXiv:2406.19054 (2024). https://pith.science/paper/RQPWXH5F
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,684 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.07 · human participants 0.54 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.04 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. Machine learning and deep learning should become accessible to millions of end users. No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. contribution 0.97 0.66 0.17 asserted 1.00
s1 Machine learning and deep learning should become accessible to millions of end users. background 0.92 0.04 0.08 asserted 0.80
s2 No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. background 0.54 0.18 0.10 asserted 0.99
s3 One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models. contribution 0.98 0.44 0.18 asserted 0.60

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.81.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. Machine learning and deep learning should become accessible to millions of end users. No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 15 sentences without a confident label

Cites as background 1 paper
  • Popular methods in active learning might be uncertainty-based [ref] , density- or diversity-based approaches [ref] , ensemble methods [ref] , and expected error reduction [ref] .

    Discriminative Active Learning · Machine Learning (ml) · 0.95

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