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Found 8 papers, 2 papers with code
Gradient based Feature Attribution in Explainable AI: A Technical Review
Due to the lack of a rigorous definition of explainable AI (XAI), a plethora of research related to explainability, interpretability, and transparency has been developed to explain and analyze the model from various perspectives.
Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously.
Explaining Language Models' Predictions with High-Impact Concepts
The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions.
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations
We then propose DualCF strategy to circumvent the above issues, which is achieved by taking not only CF but also counterfactual explanation of CF (CCF) as pairs of training samples for the substitute model.
On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation
In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks.
Ranked #1 on
Automated Essay Scoring
on ASAP
Hedging longevity risk in defined contribution pension schemes
We transform this constrained optimal investment problem into an unconstrained problem by replicating a self-financing portfolio of future contributions from the member and the minimum guarantee provided by the scheme.
Removing Stripes, Scratches, and Curtaining with Non-Recoverable Compressed Sensing
Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs.
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
We compare the different variants with our baseline model.
