Paper Citation Record · LEDGER

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer

As of 9 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 3 inbound Pith citation observations for arXiv:2502.04573.

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pith.paper-citation-record.v1
2502.04573 v2

Coverage vector

measured 89 of 89 reference resolution

Typed states for the displayed outbound observations.

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measured 92 of 92 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T14:29:04.708853Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T14:35:46.884992Z

Reference resolution

89 of 89 outbound references displayed

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External citation measurements

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Outbound references

Observation 1eccd6ad-081b-4d85-91f8-72e0371b160f · outbound

This paper cites write newline.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer write newline

Reference 1

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This paper cites and Devanbu, P.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer and Devanbu, P

Reference 2

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Observation 488f1f9b-b83a-4499-a202-4da4bfd8a787 · outbound

This paper cites and Flammarion, N.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer and Flammarion, N

Reference 3

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Unresolved cited work

Reference 4

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This paper cites G., van Rijn, J.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer G., van Rijn, J

Reference 5

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This paper cites Deep neural networks and tabular data: A survey.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Deep neural networks and tabular data: A survey

Reference 6

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This paper cites Language models are realistic tabular data generators.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Language models are realistic tabular data generators

Reference 7

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Scaling Transformer to 1M tokens and beyond with RMT

Reference 8

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This paper cites LLMs Are Few-Shot In-Context Low-Resource Language Learners.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer LLMs Are Few-Shot In-Context Low-Resource Language Learners

Reference 9

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Importance of semantic representation: Dataless classification

Reference 10

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This paper cites Z., Wu, J., and Sun, J.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Z., Wu, J., and Sun, J

Reference 11

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer and Guestrin, C

Reference 12

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This paper cites Notes from the ai frontier: Insights from hundreds of use cases.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Notes from the ai frontier: Insights from hundreds of use cases

Reference 13

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Support-vector networks

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer and Hart, P

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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This paper cites Efficient and robust automated machine learning.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Efficient and robust automated machine learning

Reference 17

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This paper cites Auto-sklearn 2.0: Hands-free automl via meta-learning.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Auto-sklearn 2.0: Hands-free automl via meta-learning

Reference 18

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Model-agnostic meta-learning for fast adaptation of deep networks

Reference 19

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This paper cites F., Feurer, M., and Bischl, B.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer F., Feurer, M., and Bischl, B

Reference 20

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Population-Based Evolution Optimizes a Meta-Learning Objective

Reference 21

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Large Scale Transfer Learning for Tabular Data via Language Modeling

Reference 22

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Meta-learning reduces the amount of data needed to build ai models in oncology

Reference 23

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Generative adversarial nets

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Explaining and Harnessing Adversarial Examples

Reference 25

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Revisiting deep learning models for tabular data

Reference 26

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer On embeddings for numerical features in tabular deep learning

Reference 27

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Tabr: Tabular deep learning meets nearest neighbors

Reference 28

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Why do tree-based models still outperform deep learning on typical tabular data? Advances in neural information processing systems, 35: 0 507--520, 2022

Reference 29

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Tabllm: Few-shot classification of tabular data with large language models

Reference 30

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Drift-resilient tab PFN : In-context learning distribution shifts on tabular data

Reference 31

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Unresolved cited work

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer u ller, S., Purucker, L., Krishnakumar, A., K \

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Multilayer feedforward networks are universal approximators

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Meta-learning in neural networks: A survey

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer TabTransformer: Tabular Data Modeling Using Contextual Embeddings

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer N., and Plaat, A

Reference 38

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer A survey on generative adversarial networks: Variants, applications, and training

Reference 40

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Categorical Reparameterization with Gumbel-Softmax

Reference 41

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Well-tuned simple nets excel on tabular datasets

Reference 42

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Well-tuned simple nets excel on tabular datasets

Reference 43

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Lightgbm: A highly efficient gradient boosting decision tree

Reference 44

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Understanding catastrophic overfitting in single-step adversarial training

Reference 45

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer J., Grinsztajn, L., and Varoquaux, G

Reference 46

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Tab DDPM : Modelling tabular data with diffusion models, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:09.014490Z

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer J., and Bengio, S

Reference 48

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Zero-data learning of new tasks

Reference 49

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Metalearning: a survey of trends and technologies

Reference 50

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Transfer Learning with Deep Tabular Models

Reference 51

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Neural architecture optimization

Reference 52

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Observation d32d2d83-7941-4c5d-bf68-d3f98f7be453 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 53

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Observation 9189d6ad-10d0-4446-a648-4c3b204aadbb · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Towards deep learning models resistant to adversarial attacks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.965206Z

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Observation a70be90c-c3e8-47e1-a78a-4323dabce444 · outbound

This paper cites Language Models are Few-Shot Learners.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Language Models are Few-Shot Learners

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.968600Z

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Observation 536d2d8a-04e7-4329-9e37-e618bd3866bf · outbound

This paper cites When do neural nets outperform boosted trees on tabular data? Advances in Neural Information Processing Systems, 36, 2024.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer When do neural nets outperform boosted trees on tabular data? Advances in Neural Information Processing Systems, 36, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.972375Z

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Observation 9ff48f83-757f-43cb-83de-d1631cecc024 · outbound

This paper cites Transformers Can Do Bayesian Inference.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Transformers Can Do Bayesian Inference

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.975675Z

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Observation 6a3871a3-a1bc-4357-92b3-25934bd22d41 · outbound

This paper cites Statistical foundations of prior-data fitted networks.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Statistical foundations of prior-data fitted networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.941638Z

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Observation 6d1ff19a-03f4-4a9a-84d4-da57d2bb4175 · outbound

This paper cites STUNT : Few-shot tabular learning with self-generated tasks from unlabeled tables.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer STUNT : Few-shot tabular learning with self-generated tasks from unlabeled tables

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.930402Z

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Observation 4220f157-59e6-404b-adec-b5b51931d92e · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer On First-Order Meta-Learning Algorithms

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.986702Z

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Observation 165dc0f1-51f4-4890-8365-c88aac0aa68b · outbound

This paper cites E., and Mitchell, T.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer E., and Mitchell, T

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.918971Z

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Observation 260ac65e-551b-496c-be3c-00bc747b816f · outbound

This paper cites True few-shot learning with language models.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer True few-shot learning with language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.908018Z

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Observation 16dc560c-68d1-49ab-a000-44ae4afa8d55 · outbound

This paper cites Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:07.997778Z

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Observation a884971f-4a12-48fd-8c4a-c0217b9f3b82 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.001797Z

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Observation a0bd913c-056b-4db4-bc8a-010b008769a7 · outbound

This paper cites V., and Gulin, A.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer V., and Gulin, A

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.005832Z

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Observation 5ab4df1b-b7c7-4556-baf0-48ff0978485f · outbound

This paper cites TabICL: A Tabular Foundation Model for In-Context Learning on Large Data.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.009691Z

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Observation 1fe21054-ca63-42ac-8519-d00e57d7f19d · outbound

This paper cites J., Burden, S.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer J., Burden, S

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.889094Z

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Observation 74c6b109-7fd8-4fcb-83b0-762adeedcc0b · outbound

This paper cites Revisiting Pretraining Objectives for Tabular Deep Learning.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Revisiting Pretraining Objectives for Tabular Deep Learning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.016902Z

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Observation 93673bfd-4fe0-43af-a311-1dcecd61b563 · outbound

This paper cites A., Xu, Z., Dickerson, J., Studer, C., Davis, L.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer A., Xu, Z., Dickerson, J., Studer, C., Davis, L

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.877356Z

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Observation 570c38e0-e234-4594-a5fb-cac88ea268a0 · outbound

This paper cites and Armon, A.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer and Armon, A

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.865980Z

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Observation 94b2a683-17c5-4f29-8290-c46a1e4cc1e7 · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.027224Z

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Observation f6f946b1-5dc5-4d86-9040-1c0b488b425d · outbound

This paper cites C., Thelin, S., and Klein, T.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer C., Thelin, S., and Klein, T

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.853865Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a3d14aa7-0488-4f13-b2f7-9567a1677212 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.034681Z

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Observation be9ac445-8da2-443c-a49c-936ed28a1478 · outbound

This paper cites Regression shrinkage and selection via the lasso.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Regression shrinkage and selection via the lasso

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.038509Z

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Observation e3ff7f27-2140-4197-bcc3-0c268659f65a · outbound

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Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-08T22:21:08.836727Z

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Observation 2a1c5f2a-d99e-428a-8389-6940fdfc01ca · outbound

This paper cites L., Cabi, S., Eslami, S., Vinyals, O., and Hill, F.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer L., Cabi, S., Eslami, S., Vinyals, O., and Hill, F

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.826753Z

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Observation 3502c078-0805-490e-84c2-0758c05fbad4 · outbound

This paper cites Meta-Learning: A Survey.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Meta-Learning: A Survey

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.048836Z

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Observation 3ee407ba-b2e8-498f-a6b0-82c12534041d · outbound

This paper cites K., Brahma, D., and Rai, P.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer K., Brahma, D., and Rai, P

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.815882Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d20bd88a-b744-4a69-8435-a37d340954c2 · outbound

This paper cites an unresolved cited work.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-08T22:21:08.804633Z

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Observation 937f2aec-b678-422f-bb97-2f75e0e9eff9 · outbound

This paper cites N., Hutchins, D., and Szegedy, C.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer N., Hutchins, D., and Szegedy, C

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.793107Z

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Observation a60e71ea-a9e5-41d0-879b-0096a80ca80d · outbound

This paper cites Zero-shot learning - the good, the bad and the ugly.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Zero-shot learning - the good, the bad and the ugly

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.781211Z

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Observation 6aa000ea-6eed-4879-9e1f-e2ea6fde13ea · outbound

This paper cites H., Schiele, B., and Akata, Z.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer H., Schiele, B., and Akata, Z

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.768622Z

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Observation 88ca2ab7-c220-4e14-b98c-4d2b7d30d8fa · outbound

This paper cites Making pre-trained language models great on tabular prediction.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Making pre-trained language models great on tabular prediction

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.755858Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ad0d049c-8d0c-4370-ab47-686dcac526ed · outbound

This paper cites Towards cross-table masked pretraining for web data mining.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Towards cross-table masked pretraining for web data mining

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.742873Z

Source-reported events for the cited work

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Observation 52bceb4c-9c23-4dbb-b5a3-c3a772fc7773 · outbound

This paper cites A closer look at deep learning on tabular data.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer A closer look at deep learning on tabular data

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.078648Z

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Observation e87858bd-e853-458e-bb50-9a1572adf6bf · outbound

This paper cites You only propagate once: Accelerating adversarial training via maximal principle.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer You only propagate once: Accelerating adversarial training via maximal principle

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.731374Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation edefe187-824f-419a-8fb9-2c13c55b2d01 · outbound

This paper cites Free adversarial training with layerwise heuristic learning.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Free adversarial training with layerwise heuristic learning

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.719763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 794d53a2-cdee-4938-a53e-639c07aa23a5 · outbound

This paper cites XTab: Cross-table Pretraining for Tabular Transformers.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer XTab: Cross-table Pretraining for Tabular Transformers

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-08T22:21:08.089771Z

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Observation e3ae12b9-6150-49b4-afec-56774acd2620 · outbound

This paper cites Varibad: Variational bayes-adaptive deep rl via meta-learning.

Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer Varibad: Variational bayes-adaptive deep rl via meta-learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:21:08.708079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation d36c0b2d-8c82-4454-b8c7-40d43d489895 · inbound

On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses cites this paper.

On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:52:15.040464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d506eb7-558b-4106-8761-535446003240 · inbound

Compositional Sparsity as an Inductive Bias for Neural Architecture Design cites this paper.

Compositional Sparsity as an Inductive Bias for Neural Architecture Design Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:35:46.886653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ece81874-52f9-449f-85c5-932e3166466c · inbound

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification cites this paper.

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T14:29:04.708853Z

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