Value Imputation and Mask Estimation
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What is VIME?
VIME stands for Value Imputation and Mask Estimation. It is a learning framework used for tabular data.…
GrowNet
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What is GrowNet?
GrowNet is a new technique that combines the power of gradient boosting with deep neural networks. It…
FT-Transformer
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FT-Transformer is a new approach to analyzing data in the tabular domain. It is an adaptation of the Transformer architecture,…
DCN-V2
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What is DCN-V2?
DCN-V2 is a type of architecture that is used in learning-to-rank. It is an improvement over the…
TabTransformer
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Introduction to TabTransformer: A Revolutionary Method of Deep Tabular Data Modeling
Tabular data modeling is an important problem in supervised…
Network On Network
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Overview of Non-Linear Interactions in Network On Network (NON)
Network On Network (NON) is a powerful tool used in practical…
SAINT
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Understanding SAINT: A Revolutionary Approach to Tabular Data Problems
SAINT, which stands for "Self-Attentive INTeraction model", is a cutting-edge deep…
TAPAS
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What are TAPAS and How Do They Work?
TAPAS is a type of weakly supervised question answering model designed to…
ARM-Net
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ARM-Net: An Overview
ARM-Net is a framework designed to analyze structured data. It utilizes a technique called adaptive relation modeling,…
SCARF
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SCARF is a powerful and effective technique for contrastive learning that has proven to be widely-applicable in modern machine learning.…
StruBERT: Structure-aware BERT for Table Search and Matching
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StruBERT: The Power of Combining Textual and Structural Information for Table Retrieval and Classification
In today's world of big data,…
MATE
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MATE is a type of Transformer architecture that has been specifically designed to help people model web tables. Its design…
TURL: Table Understanding through Representation Learning
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Overview:
TURL is a new framework that uses pre-training/fine-tuning to understand relational tables on the web. It learns deep…
TaBERT
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TaBERT: A Powerful Language Model for Natural Language and Table Data
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If you’ve ever searched for information on the…
Boost-GNN
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Boost-GNN: A Powerful Architecture for Effective Machine Learning
Understanding Boost-GNN
Machine learning has come a long way in recent years.…
DNN2LR
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Introduction to DNN2LR
As technology advances, the amount of data we collect and analyze also increases, and it can be…
Neural Oblivious Decision Ensembles
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Overview of NODE: Neural Oblivious Decision Ensembles
Neural Oblivious Decision Ensembles (NODE) is an innovative technology that leverages differentiable oblivious…
TabNet
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TabNet is a new deep learning architecture that can process large datasets in a quick and accurate way. It uses…
TABBIE
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The study of Machine Learning is constantly evolving and giving birth to new and efficient techniques to analyze and comprehend…
TabNN
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Are you interested in artificial intelligence and neural networks? If so, you might want to learn about TabNN. TabNN is…
Hierarchical Multi-Task Learning
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Hierarchical MTL: A More Effective Way of Multi-Task Learning with Deep Neural Networks
Multi-task learning (MTL) is a powerful technique…
AutoInt
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AutoInt is a deep learning method used for modeling high-order feature interactions of input features, both numerical and categorical. It…
Wide&Deep
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Wide&Deep: Combining Memorization and Generalization for Recommender Systems
Wide&Deep is a method used to train wide linear models…
Self-Normalizing Neural Networks
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Overview of Self-Normalizing Neural Networks (SNNs)
If you've ever heard of neural networks, you may understand that they can be…
online deep learning
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The Challenge of Learning with Deep Neural Networks
For many years, deep neural networks (DNNs) have been trained using a…
Bidirectional LSTM
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A **Bidirectional LSTM** is a type of sequence processing model that uses two Long Short-Term Memory (LSTM) layers to process…