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Found 8 papers, 7 papers with code
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
As a result, classification methods usually perform poorly with these small datasets, leading to weak predictive performance.
TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
To overcome this challenge, we introduce TabMDA, a novel method for manifold data augmentation on tabular data.
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs
Variational Autoencoders and their many variants have displayed impressive ability to perform dimensionality reduction, often achieving state-of-the-art performance.
ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data
Tabular biomedical data poses challenges in machine learning because it is often high-dimensional and typically low-sample-size (HDLSS).
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
Tabular biomedical data is often high-dimensional but with a very small number of samples.
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
We create a graph between samples for each data dimension, and utilise Graph Neural Networks (GNNs) to extract this implicit structure, and for conditioning the parameters of the first layer of an underlying predictor network.
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets.
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer.
