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Found 10 papers, 1 papers with code
Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models
Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records.
SANVis: Visual Analytics for Understanding Self-Attention Networks
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
Modeling Disease Progression Trajectories from Longitudinal Observational Data
We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods.
An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences
In this paper, we conduct an empirical study on a bias measure, log-likelihood Masked Language Model (MLM) scoring, on a benchmark dataset.
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation
We call the unwanted correlations "data biases," and the visual features causing data biases "bias factors."
Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration
Machine learning models built on training data with multiple modalities can reveal new insights that are not accessible through unimodal datasets.
ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
Generative image models have emerged as a promising technology to produce realistic images.
MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models
We conclude with a discussion on the generalizability and trustworthiness of the MiMICRI framework, as well as the implications of our findings on the development of domain-centered XAI methods for model interpretability in healthcare contexts.
