ORIGAMI: A generative transformer architecture for predictions from semi-structured data | Paper Citation Record

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This paper cites The DEformer: An Order-Agnostic Distribution Estimating Transformer.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data The DEformer: An Order-Agnostic Distribution Estimating Transformer

Reference 1

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Tabnet: Attentive interpretable tabular learning

Reference 2

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This paper cites Transformers for tabular data representation: A tutorial on models and applications.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Transformers for tabular data representation: A tutorial on models and applications

Reference 3

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data The JavaScript Object Notation (JSON) Data Interchange Format

Reference 4

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This paper cites XGBoost: A Scalable Tree Boosting System.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data XGBoost: A Scalable Tree Boosting System

Reference 5

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This paper cites TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data

Reference 6

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This paper cites Large language models (LLMs) on tabular data: Prediction, generation, and understanding - a survey.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Large language models (LLMs) on tabular data: Prediction, generation, and understanding - a survey

Reference 7

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data DDXPlus: A new dataset for automatic medical diagnosis

Reference 8

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 9

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data A new algorithm for data compression

Reference 10

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Convolutional Sequence to Sequence Learning

Reference 11

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Goller and A

Reference 12

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Unresolved cited work

Reference 13

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This paper cites Long Short-Term Memory.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Long Short-Term Memory

Reference 14

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This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 15

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Multilayer feedforward networks are uni- versal approximators

Reference 16

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This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 17

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This paper cites LightGBM: A Highly Efficient Gradient Boosting Decision Tree.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Reference 18

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Adam: A Method for Stochastic Optimization

Reference 19

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This paper cites Automata-based constraints for language model decoding.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Automata-based constraints for language model decoding

Reference 20

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This paper cites TabDDPM: Modelling Tabular Data with Diffusion Models.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data TabDDPM: Modelling Tabular Data with Diffusion Models

Reference 21

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This paper cites The Neural Autoregressive Distribution Estimator.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data The Neural Autoregressive Distribution Estimator

Reference 22

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This paper cites TreeRNN: Topology-preserving deep graph embedding and learning.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data TreeRNN: Topology-preserving deep graph embedding and learning

Reference 23

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This paper cites JsonGrinder.jl: Automated differentiable neural architecture for embedding arbitrary JSON data.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data JsonGrinder.jl: Automated differentiable neural architecture for embedding arbitrary JSON data

Reference 24

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data The UCI Machine Learning Repository

Reference 25

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This paper cites Pedregosa, G.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Pedregosa, G

Reference 26

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This paper cites Approximation capability of neural networks on spaces of probability measures and tree-structured domains, June 2019.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Approximation capability of neural networks on spaces of probability measures and tree-structured domains, June 2019

Reference 27

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This paper cites CatBoost: Unbiased boosting with categorical features.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data CatBoost: Unbiased boosting with categorical features

Reference 28

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Unresolved cited work

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This paper cites Improving Language Understanding by Generative Pre-Training,.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Improving Language Understanding by Generative Pre-Training,

Reference 30

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data The graph neural network model

Reference 31

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Novel positional encodings to enable tree-based transformers

Reference 32

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Tabular data: Deep learning is not all you need

Reference 33

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Introduction to the Theory of Computation

Reference 34

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 35

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Reference 36

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Roformer: Enhanced transformer with rotary position embedding

Reference 37

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Unresolved cited work

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data A deep and tractable density estimator

Reference 39

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 40

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Statistical learning theory: Models, concepts, and results

Reference 41

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data A Survey on Self-Supervised Learning for Non-Sequential Tabular Data

Reference 42

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This paper cites Willard and Rémi Louf.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Willard and Rémi Louf

Reference 43

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This paper cites Deep Learning on Semi-Structured Data and Its Applications to Video-game AI.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Deep Learning on Semi-Structured Data and Its Applications to Video-game AI

Reference 44

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This paper cites A Framework for End-to-End Learning on Semantic Tree-Structured Data.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data A Framework for End-to-End Learning on Semantic Tree-Structured Data

Reference 45

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 46

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data Modeling tabular data using conditional GAN

Reference 47

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 48

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ORIGAMI: A generative transformer architecture for predictions from semi-structured data AGE": 12,

Reference 49

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This paper cites URL https://cdn.openai.com/research-covers/language-unsupervised/ language_understanding_paper.pdf.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data URL https://cdn.openai.com/research-covers/language-unsupervised/ language_understanding_paper.pdf

Reference 2018

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This paper cites Efficient Guided Generation for Large Language Models.

ORIGAMI: A generative transformer architecture for predictions from semi-structured data Efficient Guided Generation for Large Language Models

Reference 2023

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