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Found 8 papers, 4 papers with code
Sequentially Controlled Text Generation
While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure.
DeHumor: Visual Analytics for Decomposing Humor
Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e. g., pause).
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable.
Interpretable and Steerable Sequence Learning via Prototypes
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.
RuleMatrix: Visualizing and Understanding Classifiers with Rules
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable.
Understanding Hidden Memories of Recurrent Neural Networks
We propose a technique to explain the function of individual hidden state units based on their expected response to input texts.
