Pith. Sign in

RECORD

A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Hannes Kath, Bengt Lüers, Thiago S. Gouvêa, Daniel Sonntag

Human-Computer Interaction · Submitted 24 May 2023

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

The authors' main claim Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent.

how it is stated · asserted 0.99 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2305.15353 (version 1)
Pith IDDKAAXAOY, Pith's permanent record of this paper and its review
CitationBengt L\"uers, Daniel Sonntag, Hannes Kath, Thiago S. Gouv\^ea. "A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models." arXiv:2305.15353 (2023). https://pith.science/paper/DKAAXAOY
Claim paper

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.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,084 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.09 · human participants 0.51 · controlled comparison 0.02 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.04 · mentions code or data availability 0.03.

The abstract, sentence by sentence

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

Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. A dataset is represented as a cloud of points in virtual space. The user explores the cloud through movement and uses hand gestures to categorise portions of the cloud. This triggers gradual movements in the cloud: points of the same category are attracted to each other, different groups are pushed apart, while points are globally distributed in a way that utilises the entire space. The space, time, and forces observed in virtual reality can be mapped to well-defined machine learning concepts, namely the latent space, the training epochs and the backpropagation. Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent. We expect this approach to accelerate the autonomous development of deep learning applications by end users in novel areas.

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 Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. background 0.96 0.05 0.04 asserted 1.00
s1 We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. contribution 0.99 0.91 0.13 asserted 1.00
s2 A dataset is represented as a cloud of points in virtual space. method 0.97 0.61 0.03 asserted 1.00
s3 The user explores the cloud through movement and uses hand gestures to categorise portions of the cloud. method 0.99 0.56 0.04 asserted 1.00
s4 This triggers gradual movements in the cloud: points of the same category are attracted to each other, different groups are pushed apart, while points are globally distributed in a way that utilises the entire space. method 0.97 0.81 0.08 asserted 1.00
s5 The space, time, and forces observed in virtual reality can be mapped to well-defined machine learning concepts, namely the latent space, the training epochs and the backpropagation. interpretation 0.85 0.69 0.15 asserted 0.99
s6 Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent. interpretation 0.91 0.73 0.47 asserted 0.99
s7 We expect this approach to accelerate the autonomous development of deep learning applications by end users in novel areas. interpretation 0.59 0.09 0.36 hedged possibility 0.64

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.54.

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

Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. A dataset is represented as a cloud of points in virtual space. The user explores the cloud through movement and uses hand gestures to categorise portions of the cloud. This triggers gradual movements in the cloud: points of the same category are attracted to each other, different groups are pushed apart, while points are globally distributed in a way that utilises the entire space. The space, time, and forces observed in virtual reality can be mapped to well-defined machine learning concepts, namely the latent space, the training epochs and the backpropagation. Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent. We expect this approach to accelerate the autonomous development of deep learning applications by end users in novel areas.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

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

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.