# WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

arXiv: https://arxiv.org/abs/2306.09328 · HTML: https://mlsanity.com/paper/2306.09328/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://mlsanity.com/paper/2210.14896/) (2022)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2104.14548/) (2021)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://mlsanity.com/paper/1711.07131/) (2017)
- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection](https://mlsanity.com/paper/1902.00038/) (2019)
- [Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2](https://mlsanity.com/paper/2210.00586/) (2022)
- [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016)
- [Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples](https://mlsanity.com/paper/1912.04853/) (2019)
- [EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection](https://mlsanity.com/paper/1808.09074/) (2018)
- [Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces](https://mlsanity.com/paper/2202.02641/) (2022)
- [embComp: Visual Interactive Comparison of Vector Embeddings](https://mlsanity.com/paper/1911.01542/) (2019)
- [Visual Comparison of Language Model Adaptation](https://mlsanity.com/paper/2208.08176/) (2022)
- [Characterizing Automated Data Insights](https://mlsanity.com/paper/2008.13060/) (2020)
- [NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks](https://mlsanity.com/paper/2108.12931/) (2021)
- [Angler: Helping Machine Translation Practitioners Prioritize Model Improvements](https://mlsanity.com/paper/2304.05967/) (2023)
- [NOVA: A Practical Method for Creating Notebook-Ready Visual Analytics](https://mlsanity.com/paper/2205.03963/) (2022)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)

## Built on by (most cited first, 28 of 28)

- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2403.06098/) (2024)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation](https://mlsanity.com/paper/2503.01739/) (2025)
- [Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics](https://mlsanity.com/paper/2307.02502/) (2023)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation](https://mlsanity.com/paper/2411.04709/) (2024)
- [Embedding Atlas: Low-Friction, Interactive Embedding Visualization](https://mlsanity.com/paper/2505.06386/) (2025)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors](https://mlsanity.com/paper/2409.18203/) (2024)
- [A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations](https://mlsanity.com/paper/2407.17876/) (2024)
- [A Scalable Approach to Clustering Embedding Projections](https://mlsanity.com/paper/2504.07285/) (2025)
- [Evalet: Evaluating Large Language Models through Functional Fragmentation](https://mlsanity.com/paper/2509.11206/) (2025)
- [JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets](https://mlsanity.com/paper/2407.03045/) (2024)
- [GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts](https://mlsanity.com/paper/2509.08818/) (2025)
- [ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalization](https://mlsanity.com/paper/2510.18433/) (2025)
- [Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Concepts](https://mlsanity.com/paper/2511.06048/) (2025)
- [VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation](https://mlsanity.com/paper/2406.04808/) (2024)
- [Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame](https://mlsanity.com/paper/2406.06574/) (2024)
- [Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations](https://mlsanity.com/paper/2504.10745/) (2025)
- [StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images](https://mlsanity.com/paper/2406.13735/) (2024)
- [Interpretable Syntactic Representations Enable Hierarchical Word Vectors](https://mlsanity.com/paper/2411.08384/) (2024)
- [ClusterChat: Multi-Feature Search for Corpus Exploration](https://mlsanity.com/paper/2412.14533/) (2024)
- [AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings](https://mlsanity.com/paper/2505.14664/) (2025)
- [Graphing the Truth: Structured Visualizations for Automated Hallucination Detection in LLMs](https://mlsanity.com/paper/2512.00663/) (2025)
- [LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks](https://mlsanity.com/paper/2607.21941/) (2026)
- [LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations](https://mlsanity.com/paper/2609.12364/) (2026)
- [ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images](https://mlsanity.com/paper/2609.27371/) (2026)

## Abstract (arXiv)

Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users' web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: https://poloclub.github.io/wizmap.