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Found 6 papers, 0 papers with code
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
We propose and study a minimalist approach towards synthetic tabular data generation.
Multi-resolution Interpretation and Diagnostics Tool for Natural Language Classifiers
Developing explainability methods for Natural Language Processing (NLP) models is a challenging task, for two main reasons.
Model Explainability in Deep Learning Based Natural Language Processing
We then applied one of the NLP explainability methods Layer-wise Relevance Propagation (LRP) to a NLP classification model.
Bias, Fairness, and Accountability with AI and ML Algorithms
In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms.
Survey of Imbalanced Data Methodologies
Imbalanced data set is a problem often found and well-studied in financial industry.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
