TabPFN is a Prior-Data Fitted Network that approximates Bayesian inference for small tabular classification by training a Transformer once on synthetic data drawn from a causal prior, then solves new tasks in a single forward pass without further updates.
Deep Neural Networks and Tabular Data: A Survey
4 Pith papers cite this work, alongside 29 external citations. Polarity classification is still indexing.
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AXIL computes exact fixed-structure instance attributions for squared-error GBMs via a matrix-free O(TN) backward operator, outperforming BoostIn/TREX/LeafInfluence on 20 regression datasets.
FCNN maps tabular features to fuzzy memberships, arranges them as images, and uses CNNs to classify, reporting competitive or superior results versus DT, SVM, FNN, Bayes, and RF on six generated noisy datasets.
Class imbalance causes DNNs to underfit minority classes early in training and produce non-generalizable minority representations later by overfitting to minimize overall loss.
citing papers explorer
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TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
TabPFN is a Prior-Data Fitted Network that approximates Bayesian inference for small tabular classification by training a Transformer once on synthetic data drawn from a causal prior, then solves new tasks in a single forward pass without further updates.
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AXIL: Exact Instance Attribution for Gradient Boosting
AXIL computes exact fixed-structure instance attributions for squared-error GBMs via a matrix-free O(TN) backward operator, outperforming BoostIn/TREX/LeafInfluence on 20 regression datasets.
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Fuzzy Convolution Neural Networks for Tabular Data Classification
FCNN maps tabular features to fuzzy memberships, arranges them as images, and uses CNNs to classify, reporting competitive or superior results versus DT, SVM, FNN, Bayes, and RF on six generated noisy datasets.
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On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
Class imbalance causes DNNs to underfit minority classes early in training and produce non-generalizable minority representations later by overfitting to minimize overall loss.