Xtab: Cross-table pretraining for tabular transformers
The success of self-supervised learning in computer vision and natural language processing
has motivated pretraining methods on tabular data. However, most existing tabular self …
has motivated pretraining methods on tabular data. However, most existing tabular self …
A survey on masked autoencoder for self-supervised learning in vision and beyond
Masked autoencoders are scalable vision learners, as the title of MAE\cite {he2022masked},
which suggests that self-supervised learning (SSL) in vision might undertake a similar …
which suggests that self-supervised learning (SSL) in vision might undertake a similar …
Stunt: Few-shot tabular learning with self-generated tasks from unlabeled tables
Learning with few labeled tabular samples is often an essential requirement for industrial
machine learning applications as varieties of tabular data suffer from high annotation costs …
machine learning applications as varieties of tabular data suffer from high annotation costs …
Self-supervised representation learning from random data projectors
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting
the transformation invariance assumption under artificially designed data augmentations …
the transformation invariance assumption under artificially designed data augmentations …
TIP: Tabular-image pre-training for multimodal classification with incomplete data
Images and structured tables are essential parts of real-world databases. Though tabular-
image representation learning is promising to create new insights, it remains a challenging …
image representation learning is promising to create new insights, it remains a challenging …
Remasker: Imputing tabular data with masked autoencoding
We present ReMasker, a new method of imputing missing values in tabular data by
extending the masked autoencoding framework. Compared with prior work, ReMasker is …
extending the masked autoencoding framework. Compared with prior work, ReMasker is …
Modality-agnostic self-supervised learning with meta-learned masked auto-encoder
Despite its practical importance across a wide range of modalities, recent advances in self-
supervised learning (SSL) have been primarily focused on a few well-curated domains, eg …
supervised learning (SSL) have been primarily focused on a few well-curated domains, eg …
Stochastic re-weighted gradient descent via distributionally robust optimization
We develop a re-weighted gradient descent technique for boosting the performance of deep
neural networks. Our algorithm involves the importance weighting of data points during each …
neural networks. Our algorithm involves the importance weighting of data points during each …
A Comprehensive Survey on Data Augmentation
Data augmentation is a series of techniques that generate high-quality artificial data by
manipulating existing data samples. By leveraging data augmentation techniques, AI …
manipulating existing data samples. By leveraging data augmentation techniques, AI …
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
While deep learning (DL) models are state-of-the-art in text and image domains, they have
not yet consistently outperformed Gradient Boosted Decision Trees (GBDTs) on tabular …
not yet consistently outperformed Gradient Boosted Decision Trees (GBDTs) on tabular …