Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

5 February 2022

Papers citing "Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces"

8 / 8 papers shown

Title
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 63 3 0 06 Aug 2024
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis Shashank Kotyan Tatsuya Ueda Danilo Vasconcellos Vargas 32 1 0 07 Dec 2023
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings Zijie J. Wang Fred Hohman Duen Horng Chau 37 21 0 15 Jun 2023
Technical Understanding from IML Hands-on Experience: A Study through a Public Event for Science Museum Visitors Wataru Kawabe Yuri Nakao Akihisa Shitara Yusuke Sugano 36 1 0 10 May 2023
VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Space David Chuan-En Lin Fabian Caba Heilbron Joon-Young Lee Oliver Wang Nikolas Martelaro VGen 44 4 0 22 Nov 2022
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings Agapi Rissaki Bruno Scarone David Liu Aditeya Pandey Brennan Klein Tina Eliassi-Rad M. Borkin FaML 21 6 0 12 Oct 2022
Visual Comparison of Language Model Adaptation Rita Sevastjanova E. Cakmak Shauli Ravfogel Ryan Cotterell Mennatallah El-Assady VLM 49 16 0 17 Aug 2022
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective Jinbin Huang Aditi Mishra Bum Chul Kwon Chris Bryan FAtt HAI 46 31 0 04 Apr 2022