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Found 7 papers, 1 papers with code
SyntaxShap: Syntax-aware Explainability Method for Text Generation
We adopt a model-based evaluation to compare SyntaxShap and its weighted form to state-of-the-art explainability methods adapted to text generation tasks, using diverse metrics including faithfulness, complexity, coherency, and semantic alignment of the explanations to the model.
PowerGraph: A power grid benchmark dataset for graph neural networks
To this aim, we develop a graph dataset for cascading failure events, which are the major cause of blackouts in electric power grids.
Generative Explanations for Graph Neural Network: Methods and Evaluations
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions.
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
Nearest neighbor search with compact codes: A decoder perspective
Modern approaches for fast retrieval of similar vectors on billion-scaled datasets rely on compressed-domain approaches such as binary sketches or product quantization.
