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Found 6 papers, 6 papers with code
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Deep learning (DL) models for tabular data problems (e. g. classification, regression) are currently receiving increasingly more attention from researchers.
TabDDPM: Modelling Tabular Data with Diffusion Models
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities.
Revisiting Pretraining Objectives for Tabular Deep Learning
Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT).
On Embeddings for Numerical Features in Tabular Deep Learning
We start by describing two conceptually different approaches to building embedding modules: the first one is based on a piecewise linear encoding of scalar values, and the second one utilizes periodic activations.
Label-Efficient Semantic Segmentation with Diffusion Models
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance.
Revisiting Deep Learning Models for Tabular Data
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets.
