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TabR: Tabular Deep Learning Meets Nearest Neighbors
2024-12-12
Summary
The paper presents a retrieval-augmented generation (RAG) inspired model for tabular data classification. The authors propose a new mechanism for similarity calculation and retrieval and show superior performance compared to previous DL models and GBDT family on previously proposed tabular benchmarks.
A Retrieval Augmented Generation (RAG) inspired for tabular data classification.
Modified mechanism for similarity calculation (and thus retrieval).
Approach
Resnet + clustering-like for RAG type.
Attention-like mechanism to calculate similarity scores.
Retrieval-based mechanism for tabular data classification
Retrieval Mechanism
Start with vanilla attention mechanism
$$
\mathcal{S}(\tilde x, \tilde x_i) = W_Q(\tilde x)^TW_K(\tilde x_i) \cdot d^{-1/2} \quad \mathcal{V}(\tilde x, \tilde x_i, y_i) = W_V(\tilde x_i)
$$
This step is adding more information about the target sample $x$ into the value vector. $W_Y(y_i)$ is the _raw contribution_ of sample $i$ (because it tells us about the label associated with that sample), where $T(W_K(\tilde x) - W_K(\tilde x_i))$ is the _correction_ term. $T(\cdot)$ translates the differences of $x$ and $x_i$ in the key-space ($W_K$) to the _label space_ ($W_Y$).
Remove the scaling term \(d^{-1/2}\) (artifact of vanilla attention anyways) from the similarity calculation.
$$
k = W_K(\tilde x), k_i = W_K(\tilde x_i) \quad \mathcal{S}(\tilde x, \tilde x_i) = - \Vert k-k_i \Vert^2 \quad \mathcal{V}(\tilde x, \tilde x_i, y_i) = W_Y(y_i) + T(k-k_1)
$$
Putting it together
The output of the retrieval module is then the weighted sum of the value vectors of top \(m\) samples, where the similarity score determines the weights (the similarity score is bound in \([0, 1]\) since we take the L2 norm).
$$
\hat y = \text{Predictor}(\tilde x + \sum \limits_{i \in \text{top m}} \mathcal{S}(\tilde x, \tilde x_i) \cdot \mathcal{V}(\tilde x, \tilde x_i, y_i))
$$
Ablations
Freeze context (encoded samples) after training starts to stablize.
Online setting (start with limited data and add unseen)
Findings
TabR and previous DL models compared against XGB
TabR and previous DL models compared against GBDTs with and without HPO.
Adding all of the 4 modifications is what makes TabR perform well.
TabR beats GBDTs on some datasets.
Numerical embeddings and retrieval seem to be key techniques in good DL performance.
Noted limitations
While the new module is more efficient than standard attention, may still not scale too well.
Resources
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