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Found 9 papers, 1 papers with code
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.
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.
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.
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.
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."
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.
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.
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.
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.
