tabular-classification
12 papers with code • 6 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in tabular-classification
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Libraries
Use these libraries to find tabular-classification models and implementations
AjwaOrMedjool
Most implemented papers
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
lucidrains/tab-transformer-pytorch
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We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning.
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
automl/tabpfn
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We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.
Generative Imputation and Stochastic Prediction
mkachuee/GenerativeImputationStochasticPrediction
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In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling
radi-cho/gatedtabtransformer
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There is an increasing interest in the application of deep learning architectures to tabular data.
GANDALF: Gated Adaptive Network for Deep Automated Learning of Features
manujosephv/GATE
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We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF).
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
peterbhase/InterpretableNLP-ACL2020
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ACL 2020
Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that combines explanations from each method.
CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations
We believe an actionable recourse should be created based on sound counterfactual explanations originating from the distribution of the ground-truth data and linked to the domain knowledge.
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
echoyi/rps_lje
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NeurIPS 2021
Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
We study the application of large language models to zero-shot and few-shot classification of tabular data.
