VIS Full Papers: Explainable AI and Machine Learning

Session chair: Hendrik Strobelt

Live-stream room: Bourbon

2021-10-27T13:00:00Z – 2021-10-27T14:30:00ZGMT-0600Change your timezone on the schedule page
2021-10-27T13:00:00Z – 2021-10-27T14:30:00Z


AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing

Hamza Elhamdadi

recorded
2021-10-27T13:00:00Z – 2021-10-27T13:15:00ZGMT-0600Change your timezone on the schedule page

Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models

Zhenge Zhao

recorded
2021-10-27T13:15:00Z – 2021-10-27T13:30:00ZGMT-0600Change your timezone on the schedule page

Towards Visual Explainable Active Learning for Zero-Shot Classification

Shichao Jia

recorded
2021-10-27T13:30:00Z – 2021-10-27T13:45:00ZGMT-0600Change your timezone on the schedule page

M^2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis

Xingbo Wang

recorded
2021-10-27T13:45:00Z – 2021-10-27T14:00:00ZGMT-0600Change your timezone on the schedule page

NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks

Haekyu Park

recorded
2021-10-27T14:00:00Z – 2021-10-27T14:15:00ZGMT-0600Change your timezone on the schedule page

Visual Analytics for RNN-Based Deep Reinforcement Learning

Junpeng Wang

recorded
2021-10-27T14:15:00Z – 2021-10-27T14:30:00ZGMT-0600Change your timezone on the schedule page

You may want to also jump to the parent event to see related presentations: VIS Full Papers

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