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<!DOCTYPE html>
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<title>3rd VISxAI Workshop at IEEE VIS 2020</title>
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content="The role of visualization in artificial intelligence (AI) gained significant attention in recent years. With the growing complexity of AI models, the critical need for understanding their inner-workings has increased. Visualization is potentially a powerful technique to fill such a critical need. The goal of this workshop is to initiate a call for 'explainables' / 'explorables' that explain how AI techniques work using visualization. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems."/>
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VISxAI is back! Join us at <a href="http://visxai.io">VISxAI 2021 at IEEE VIS</a> in New Orleans, Louisiana!
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<a class="nav-link" href="2018.html">2018</a>
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<!--<img src="img/logo.png" height="70"/>-->
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<h2>3<sup>rd</sup> Workshop on <br> <b>Visualization for AI Explainability</b></h2>
<p>October 26, 2020 at IEEE VIS <s>in Salt Lake City, Utah</s> Online</p>
<!-- <p class="text-center" style="font-size: 14pt;">
<b>PROGRAM IS ONLINE. <a href="program.html"> CLICK HERE !!!</a> </b>
</p> -->
<p>
The role of visualization in artificial intelligence (AI) gained
significant attention in recent years. With the growing complexity of AI
models, the critical need for understanding their inner-workings has
increased. Visualization is potentially a powerful technique to fill
such a critical need.
</p>
<p>
The goal of this workshop is to initiate a call for <i>"explainables" / "explorables"</i> that
explain how AI techniques work using visualization. We believe the VIS
community can leverage their expertise in creating visual narratives to
bring new insight into the often obfuscated complexity of AI systems.
</p>
<!--<p class="text-center" style="font-size: 14pt;">-->
<!--<b>PROGRAM IS ONLINE. <a href="program.html"> CLICK HERE !!!</a> </b>-->
<!--</p>-->
<p class="text-center">
<img class="img-fluid" src="img/examples-2020.png">
<div class="figure-caption">Example interactive visualization articles that explain general concepts and communicate experimental insights when playing with AI models.
(a) <a href="https://distill.pub/2019/visual-exploration-gaussian-processes/" target="_blank">A Visual Exploration of Gaussian Processes</a> by Görtler, Kehlbeck, and Deussen;
(b) <a href="https://idyll.pub/post/dimensionality-reduction-293e465c2a3443e8941b016d/" target="_blank">The Beginner's Guide to Dimensionality Reduction</a> by Conlen and Hohman;
(c) <a href="https://theo-jaunet.github.io/MemoryReduction/" target="_blank">What if we Reduce the Memory of an Artificial Doom Player?</a> by Jaunet, Vuillemot, and Wolf;
(d) <a href="https://pair-code.github.io/interpretability/uncertainty-over-space/" target="_blank">Communicating Model Uncertainty Over Space</a> by Pearce;
(e) <a href="https://parametric.press/issue-01/the-myth-of-the-impartial-machine/" target="_blank">The Myth of the Impartial Machine</a> by Feng and Wu;
(f) <a href="http://formafluens.io/client/mix10.html">FormaFluens Data Experiment</a> by Strobelt, Phibbs, and Martino.
</div>
</p>
<h2 id="dates">Important Dates</h2>
<p><i>Note: Dates have been revised due to the <a href="https://www.cdc.gov/coronavirus/2019-ncov/index.html">COVID-19 outbreak</a>.</i></p>
<pre>
<s>July 24, 2020</s> August 24, 2020, anywhere: Explainables Submission
<s>August 9, 2020</s> Sepember 24, 2020: Author Notification
<s>September 6, 2020</s> October 13, 2020: Camera-ready Copy for Accepted Submissions
<s>September 20, 2020</s> VIS Registration is Free for 2020
October 26 -- Workshop <s>in Salt Lake City</s> online at IEEE VIS 2020
</pre>
<h2 id="program">Program Overview</h2>
<p>
All times in MDT (UTC -6) on October 26, 2020.
<br>→ To attend, register for free at <a href="http://ieeevis.org/year/2020/info/registration/conference-registration">IEEE VIS</a>.
<br>→ <a href="calendar/VISxAI2020.ics">Add to your calendar.</a>
<br>→ <a href="https://virtual.ieeevis.org/session_a-visxai.html">Join the virtual even here!</a>
</p>
<table style="padding: 5pt;">
<tr>
<td class="schedule">12:00 -- 12:05</td>
<td><b>Welcome from the Organizers</b></td>
</tr>
<tr>
<td class="schedule">12:05 -- 1:00</td>
<td><b>Keynote: Thomas Wolf (Huggingface Inc.)</b>
<br>
<b>Facilitating Interactive Explanations with Open-source Libraries: An Introduction to Transfer Learning in NLP and HuggingFace</b>
<!-- <br>
Abstract: TBD -->
</td>
</tr>
<tr>
<td class="schedule">1:00 -- 1:30</td>
<td><b>Session I</b>
<br>
<a href="https://tiga1231.github.io/umap-tour/">
Comparing DNNs with UMAP Tour
</a> -- Mingwei Li and Carlos Scheidegger
<br>
<a href="https://www.pewresearch.org/interactives/how-does-a-computer-see-gender/">
How Does a Computer "See" Gender?
</a> -- Stefan Wojcik, Emma Remy, and Chris Baronavski
<br>
</td>
</tr>
<tr>
<td class="schedule">1:30 -- 2:00</td>
<td><b>Break</b></td>
</tr>
<tr>
<td class="schedule">2:00 -- 2:30</td>
<td><b>Session II</b>
<br>
<a href="https://theo-jaunet.github.io/theo-guesser/">
Théo Guesser
</a> -- Théo Jaunet, Romain Vuillemot, and Christian Wolf
<br>
<a href="http://visxai-submission.xyz/article">
Shared Interest: Human Annotations vs. AI Saliency
</a> -- Angie Boggust, Benjamin Hoover, Arvind Satyanarayan, and Hendrik Strobelt
<br>
<a href="https://aarunku5.github.io/">
A Visual Exploration of Fair Evaluation for ML - Bridging the Gap Between Research and the Real World
</a> -- Anjana Arunkumar, Swaroop Mishra, and Chris Bryan
<br>
</td>
</tr>
<tr>
<td class="schedule">2:30 -- 3:00</td>
<td><b>Session III</b>
<br>
<a href="https://anomagram.fastforwardlabs.com/#/">
Anomagram: An Interactive Visualization for Training and Evaluating Autoencoders on the task of Anomaly Detection
</a> -- Victor Dibia
<br>
<a href="https://pair-code.github.io/interpretability/text-dream/explainable/">
What Does BERT Dream Of?
</a> -- Alex Bäuerle and James Wexler
<br>
<a href="https://patel-zeel.github.io/active-learning-visualization/">
Active Learning: A Visual Tour
</a> -- Zeel B Patel and Nipun Batra
<br>
</td>
</tr>
<tr>
<td class="schedule">3:00 -- 3:05</td>
<td><b>Closing Session</b></td>
</tr>
<tr>
<td class="schedule">3:05 -- 4:00</td>
<td><b>VISxAI Eastcoast party: location coming soon</b></td>
</tr>
</table>
<br>
<h2 id="call">Call for Participation</h2>
<p><strong>SUBMISSION CLOSED</strong></p>
<!-- <p> -->
<!-- To make our work more accessible to the general audience, we are soliciting submissions in a novel format:
blog-style posts and jupyter-like notebooks. In addition we also accept position papers in a more
traditional form.
Please contact us, if you want to submit a original work in another format. Email: <a
href="mailto:[email protected]">orga.visxai at gmail.com</a> -->
<!-- </p> -->
<div class="submit-button">
<a href="/submit.html">Submission instructions</a>
</div>
<br>
<p>
Explainable submissions (e.g., interactive articles, markup, and notebooks) are the core element of the workshop, as this
workshop aims to be a platform for explanatory visualizations focusing
on AI techniques.
</p>
<p>
Authors have the freedom to use whatever templates and formats they like. However, the narrative has to be
visual and interactive, and walk readers through a keen understanding on the ML technique or application.
Authors may wish to write a <a href="https://distill.pub">Distill-style</a> blog post (format), interactive
<a href="https://idyll-lang.org/">Idyll</a> markup, or a <a href="http://jupyter.org">Jupyter</a> or <a
href="https://beta.observablehq.com/">Observable</a> notebook that integrates code, text, and
visualization to tell the story.
</p>
<p>
Here are a few examples of visual explanations of AI methods in these types of formats:
<ul>
<li>[interactive article]
<a href="https://distill.pub/2019/visual-exploration-gaussian-processes/" target="_blank">A Visual Exploration of
Gaussian Processes</a>
</li>
<li>[interactive article]
<a href="https://distill.pub/2017/momentum/" target="_blank">Why Momentum Really Works</a>
</li>
<li>[interactive article]
<a href="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/" target="_blank">A Visual Introduction to Machine Learning</a>
</li>
<li>[interactive article]
<a href="http://formafluens.io/client/mix10.html" target="_blank">Art-Inspired Data Experiments on Neural Network Model Decay</a>
</li>
<li>[interactive article]
<a href="https://research.google.com/bigpicture/attacking-discrimination-in-ml/" target="_blank">Attacking Discrimination with Smarter Machine Learning</a>
</li>
<li>[markup]
<a href="https://parametric.press/issue-01/the-myth-of-the-impartial-machine/" target="_">The Myth of the Impartial Machine</a>
</li>
<li>[markup]
<a href="https://idyll.pub/post/visxai-dimensionality-reduction-1dbad0a67a092b007c526a45/" target="_">The Beginner's Guide to Dimensionality Reduction</a>
</li>
<li>[notebook]
<a href="https://beta.observablehq.com/@nstrayer/t-sne-explained-in-plain-javascript" target="_blank">t-SNE Explained in Plain JavaScript</a>
</li>
<li>[notebook]
<a href="https://observablehq.com/@nsthorat/how-to-build-a-teachable-machine-with-tensorflow-js?collection=@observablehq/explorables" target="_blank">How to build a Teachable Machine with TensorFlow.js</a>
</li>
<li>[notebook]
<a href="http://nbviewer.jupyter.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb" target="_blank">Titanic Machine Learning from Disaster</a>
</li>
</ul>
</p>
<p>
While these examples are informative and excellent, we hope the
Visualization & ML community will think about ways to creatively expand on
such foundational work to explain AI methods using novel interactions
and visualizations often present at IEEE VIS.
Please contact us, if you want to submit a original work in another
format. Email: <a href="mailto:[email protected]">orga.visxai (at) gmail.com</a>.
</p>
<p>
Note: We also accept more traditional papers that accompany an explainable.
Be aware that we require that the explainable must stand on its own.
The reviewers will evaluate the explainable (and might chose to ignore the paper).
</p>
<p>
In previous years, the best works were invited to submit their extended work to the
online publishing platform distill.pub to generate a cite-able
publication for authors. See <a href="https://distill.pub/2019/visual-exploration-gaussian-processes/">https://distill.pub/2019/visual-exploration-gaussian-processes/</a>.
</p>
<h2 id="hall-of-fame">Hall of Fame</h2>
Each year we award one Best Submission and two Honorable Mentions. <i>Congrats to our winners!</i>
<br><br>
<h5>VISxAI 2019</h5>
<ul>
<li>
<a href="https://theo-jaunet.github.io/MemoryReduction/">
What if we Reduce the Memory of an Artificial Doom Player?
</a> -- Theo Jaunet, Romain Vuillemot, and Christian Wolf
</li>
<li>
<a href="https://qnkxsovc.gitlab.io/prob-vis/">
Statistical Distances and Their Implications to GAN Training
</a> -- Max Daniels
</li>
<li>
<a href="https://mybinder.org/v2/gh/KrishnaswamyLab/visualization_selection/master?filepath=Selecting_the_right_tool_for_the_job.ipynb">
Selecting the right tool for the job: a comparison of visualization algorithms
</a> -- Daniel Burkhardt, Scott Gigante, and Smita Krishnaswamy
</li>
</ul>
<h5>VISxAI 2018</h5>
<ul>
<li>
<a href="https://www.jgoertler.com/visual-exploration-gaussian-processes/">
A Visual Exploration of Gaussian Processes
</a> -- Jochen Görtler, Rebecca Kehlbeck and Oliver Deussen
</li>
<li>
<a href="https://idyll.pub/post/visxai-dimensionality-reduction-1dbad0a67a092b007c526a45/">
The Beginner's Guide to Dimensionality Reduction
</a> -- Matthew Conlen and Fred Hohman
</li>
<li>
<a href="https://roadsfromabove.netlify.com/">
Roads from Above
</a> -- Greg More, Slaven Marusic and Caihao Cui
</li>
</ul>
<!-- <p> <strong>SUBMISSION CLOSED</strong></p> -->
<h2 id="orga">Organizers <span style="font-size: small">(alphabetic)</span>
</h2>
<p>
Adam Perer - Carnegie Mellon University<br />
Duen Horng (Polo) Chau - Georgia Tech<br />
Fernanda Viégas - Google Brain<br />
Fred Hohman - Georgia Tech<br />
Hendrik Strobelt - MIT-IBM Watson AI Lab<br />
Mennatallah El-Assady - University of Konstanz<br />
</p>
<h2 id="pc">Program Committee</h2>
<p>
Marco Angelini<br />
Jürgen Bernard<br />
Nan Cao<br />
Dylan Cashman<br />
Marco Cavallo<br />
Jaegul Choo<br />
Tommy Dang<br />
Angus Forbes<br />
Sebastian Gehrmann<br />
Fred Hohman<br />
Iris Howley<br />
Denis Parra<br />
Arjun Srinivasan<br />
Yang Wang<br />
</p>
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