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Found 12 papers, 1 papers with code
A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes
Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment.
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning
On the one hand, by visually examining the captions automatically generated from language-image models for an image dataset, we gain deeper insights into the semantic underpinnings of the visual contents, unearthing data biases that may be entrenched within the dataset.
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference
The inherent diversity of computation types within individual Deep Neural Network (DNN) models imposes a corresponding need for a varied set of computation units within hardware processors.
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Multi-head self-attentions are then applied to the sequence to learn the attention between patches.
Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks
Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions.
Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning
The proposed method significantly reduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBF without sacrificing the SNR.
A Domain-Theoretic Framework for Robustness Analysis of Neural Networks
A domain-theoretic framework is presented for validated robustness analysis of neural networks.
Maximal function pooling with applications
Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling strategy which is called maxfun pooling.
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images
The results showed that the learning-from-noise strategy produced better output quality than ASLDN trained with relatively high SNR reference.
Learning Furniture Compatibility with Graph Neural Networks
We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score.
Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding
While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding.
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
