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Found 8 papers, 5 papers with code
Relational Local Explanations
The majority of existing post-hoc explanation approaches for machine learning models produce independent, per-variable feature attribution scores, ignoring a critical inherent characteristics of homogeneously structured data, such as visual or text data: there exist latent inter-variable relationships between features.
Language Models are Realistic Tabular Data Generators
Tabular data is among the oldest and most ubiquitous forms of data.
BoxShrink: From Bounding Boxes to Segmentation Masks
One of the core challenges facing the medical image computing community is fast and efficient data sample labeling.
A Consistent and Efficient Evaluation Strategy for Attribution Methods
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies.
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms.
Deep Neural Networks and Tabular Data: A Survey
Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data.
Robust Deep Neural Networks for Heterogeneous Tabular Data
Although deep neural networks (DNNs) constitute the state-of-the-art in many tasks based on image, audio, or text data, their performance on heterogeneous, tabular data is typically inferior to that of decision tree ensembles.
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots.
