Diffusion models have been central to the development of recent image, video, and even text generation systems. They posses striking geometric properties that can be faithfully portrayed in low-dimensional settings. However, existing resources for explaining diffusion either require an advanced theoretical foundation or focus on their neural network architectures rather than their rich geometric properties. We introduce Diffusion Explorer, an interactive tool to explain the geometric properties of diffusion models. Users can train 2D diffusion models in the browser and observe the temporal dynamics of their sampling process. Diffusion Explorer leverages interactive animation, which has been shown to be a powerful tool for making engaging visualizations of dynamic systems, making it well suited to explaining diffusion models which represent stochastic processes that evolve over time. Diffusion Explorer is open source and a live demo is available at alechelbling.com/Diffusion-Explorer.

Large language models (LLMs) have demonstrated strong capabilities across various language tasks, notably through instruction-tuning methods. However, LLMs face challenges in visualizing complex, real-world data through charts and plots. Firstly, existing datasets rarely cover a full range of chart types, such as 3D, volumetric, and gridded charts. Secondly, supervised fine-tuning methods do not fully leverage the intricate relationships within rich datasets, including text, code, and figures. To address these challenges, we propose a hierarchical pipeline and a new dataset for chart generation. Our dataset, Text2Chart31, includes 31 unique plot types referring to the Matplotlib library, with 11.1K tuples of descriptions, code, data tables, and plots. Moreover, we introduce a reinforcement learning-based instruction tuning technique for chart generation tasks without requiring human feedback. Our experiments show that this approach significantly enhances the model performance, enabling smaller models to outperform larger open-source models and be comparable to state-of-the-art proprietary models in data visualization tasks. We make the code and dataset available at https://github.com/fatemehpesaran310/Text2Chart31.

Large language models are able to generate code for visualisations in response to user requests. This is a useful application, and an appealing one for NLP research because plots of data provide grounding for language. However, there are relatively few benchmarks, and it is unknown whether those that exist are representative of what people do in practice. This paper aims to answer that question through an empirical study comparing benchmark datasets and code from public repositories. Our findings reveal a substantial gap in datasets, with evaluations not testing the same distribution of chart types, attributes, and the number of actions. The only representative dataset requires modification to become an end-to-end and practical benchmark. This shows that new, more benchmarks are needed to support the development of systems that truly address users' visualisation needs. These observations will guide future data creation, highlighting which features hold genuine significance for users.

A good data visualization is not only a distortion-free graphical representation of data but also a way to reveal underlying statistical properties of the data. Despite its common use across various stages of data analysis, selecting a good visualization often is a manual process involving many iterations. Recently there has been interest in reducing this effort by developing models that can recommend visualizations, but they are of limited use since they require large training samples (data and visualization pairs) and focus primarily on the design aspects rather than on assessing the effectiveness of the selected visualization. In this paper, we present VizAI, a generative-discriminative framework that first generates various statistical properties of the data from a number of alternative visualizations of the data. It is linked to a discriminative model that selects the visualization that best matches the true statistics of the data being visualized. VizAI can easily be trained with minimal supervision and adapts to settings with varying degrees of supervision easily. Using crowd-sourced judgements and a large repository of publicly available visualizations, we demonstrate that VizAI outperforms the state of the art methods that learn to recommend visualizations.

This tutorial explains matplotlib's way of making plots in simplified parts so you gain the knowledge and a clear understanding of how to build and modify full featured matplotlib plots. Matplotlib is the most popular plotting library in python. Using matplotlib, you can create pretty much any type of plot. However, as your plots get more complex, the learning curve can get steeper. The goal of this tutorial is to make you understand'how plotting with matplotlib works' and make you comfortable to build full-featured plots with matplotlib. The following piece of code is found in pretty much any python code that has matplotlib plots.

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