MET: Masked Encoding for Tabular Data

• URL: http://arxiv.org/abs/2206.08564v1
• Date: Fri, 17 Jun 2022 05:51:10 GMT
• Title: MET: Masked Encoding for Tabular Data
• Abstract summary: Masked SSL for Tabular Data (MET) is based on the popular MAE approach for vision-SSL. MET achieves a new state of the art (SOTA) on five diverse datasets. We shed more light on the working of MET via experiments on carefully designed simple datasets.
• Score: 29.24328720103911
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We consider the task of self-supervised representation learning (SSL) for tabular data: tabular-SSL. Typical contrastive learning based SSL methods require instance-wise data augmentations which are difficult to design for unstructured tabular data. Existing tabular-SSL methods design such augmentations in a relatively ad-hoc fashion and can fail to capture the underlying data manifold. Instead of augmentations based approaches for tabular-SSL, we propose a new reconstruction based method, called Masked Encoding for Tabular Data (MET), that does not require augmentations. MET is based on the popular MAE approach for vision-SSL [He et al., 2021] and uses two key ideas: (i) since each coordinate in a tabular dataset has a distinct meaning, we need to use separate representations for all coordinates, and (ii) using an adversarial reconstruction loss in addition to the standard one. Empirical results on five diverse tabular datasets show that MET achieves a new state of the art (SOTA) on all of these datasets and improves up to 9% over current SOTA methods. We shed more light on the working of MET via experiments on carefully designed simple datasets.

Related papers

• SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data [0.0]
  We propose Separated-at-Birth Alignment (SeBA) for learning from scarce labeled data with a larger pool of unlabeled samples.<n>SeBA aims to separate the data into two independent, but complementary views and align the representations of one view to mirror the nearest-neighbor correspondence of the data in the second view.<n>An experimental study conducted in various benchmark datasets demonstrates that SeBA achieves the state-of-the-art performance in the majority of cases.
  arXiv  Detail & Related papers  (2026-05-08T22:03:31Z)
• Fine-tuning MLLMs Without Forgetting Is Easier Than You Think [72.59321247529975]
  We design a 2x2 experimental framework to assess model performance across in-distribution and out-of-distribution image and text inputs.<n>Our results show that appropriate regularization, such as constraining the number of trainable parameters or adopting a low learning rate, effectively prevents forgetting when dealing with out-of-distribution images.<n>We attribute this forgetting as task-specific overfitting and address this issue by introducing a data-hybrid training strategy.
  arXiv  Detail & Related papers  (2026-03-15T17:16:19Z)
• Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models [85.64873567417396]
  We introduce Mitra, a TFM trained on a curated mixture of synthetic priors selected for their diversity, distinctiveness, and performance on real-world data.<n>Mitra consistently outperforms state-of-the-art TFMs, such as TabPFNv2 and TabICL, across both classification and regression benchmarks.
  arXiv  Detail & Related papers  (2025-10-24T07:15:06Z)
• TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding [52.59372043981724]
  TableDART is a training-efficient framework that integrates multimodal views by reusing pretrained single-modality models.<n>In addition, we propose a novel agent to cross-modal knowledge integration by analyzing outputs from text- and image-based models.
  arXiv  Detail & Related papers  (2025-09-18T07:00:13Z)
• TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization [2.1067477213933503]
  TabGLM (Tabular Graph Language Model) is a novel multi-modal architecture designed to model both structural and semantic information from a table. It transforms each row of a table into a fully connected graph and serialized text, which are encoded using a graph neural network (GNN) and a text encoder, respectively. Evaluations across 25 benchmark datasets demonstrate substantial performance gains.
  arXiv  Detail & Related papers  (2025-02-26T05:32:45Z)
• A Closer Look at TabPFN v2: Strength, Limitation, and Extension [51.08999772842298]
  Tabular Prior-data Fitted Network v2 (TabPFN v2) achieves unprecedented in-context learning accuracy across multiple datasets. In this paper, we evaluate TabPFN v2 on over 300 datasets, confirming its exceptional generalization capabilities on small- to medium-scale tasks.
  arXiv  Detail & Related papers  (2025-02-24T17:38:42Z)
• Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models [15.603556124006479]
  We propose retrieval-augmented language models for scalable TabICL. Our approach incorporates a customized retrieval module, combined with retrieval-guided instruction-tuning for LLMs. This enables LLMs to effectively leverage larger datasets, achieving significantly improved performance across 69 widely recognized datasets.
  arXiv  Detail & Related papers  (2025-02-05T13:16:41Z)
• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• A Closer Look at Deep Learning on Tabular Data [52.50778536274327]
  Tabular data is prevalent across various domains in machine learning. Deep Neural Network (DNN)-based methods have shown promising performance comparable to tree-based ones.
  arXiv  Detail & Related papers  (2024-07-01T04:24:07Z)
• A Survey on Self-Supervised Learning for Non-Sequential Tabular Data [15.796140543132196]
  Self-supervised learning (SSL) has been incorporated into many state-of-the-art models in various domains. This survey aims to systematically review and summarize the recent progress and challenges of SSL for non-sequential data (SSL4NS-TD) We first present a formal definition of NS-TD and clarify its correlation to related studies. Then, these approaches are categorized into three groups - predictive learning, contrastive learning, and hybrid learning, with their motivations and strengths of representative methods in each direction.
  arXiv  Detail & Related papers  (2024-02-02T08:17:41Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces [43.67453625260335]
  We propose a novel approach to few-shot learning involving knowledge sharing between datasets with heterogeneous feature spaces. FLAT learns low-dimensional embeddings of datasets and their individual columns, which facilitate knowledge transfer and generalization to previously unseen datasets. A decoder network parametrizes the predictive target network, implemented as a Graph Attention Network, to accommodate the heterogeneous nature of tabular datasets.
  arXiv  Detail & Related papers  (2023-11-16T17:45:59Z)
• Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation [67.30538142519067]
  We propose Tabular data Pre-Training via Meta-representation (TabPTM) A deep neural network is then trained to associate these meta-representations with dataset-specific classification confidences. Experiments validate that TabPTM achieves promising performance in new datasets, even under few-shot scenarios.
  arXiv  Detail & Related papers  (2023-10-31T18:03:54Z)
• TabuLa: Harnessing Language Models for Tabular Data Synthesis [4.539846270369207]
  Tabula is a tabular data synthesizer that leverages the structure of large language models (LLMs) Unlike state-of-the-art (SOTA) LLMs, Tabula discards the pre-trained weights originally designed for natural language tasks. experiments show that Tabula achieves superior synthetic data utility compared to current SOTA methods.
  arXiv  Detail & Related papers  (2023-10-19T13:50:56Z)
• PTab: Using the Pre-trained Language Model for Modeling Tabular Data [5.791972449406902]
  Recent studies show that neural-based models are effective in learning contextual representation for Tabular data. We propose a novel framework PTab, using the Pre-trained language model to model Tabular data. Our method has achieved a better average AUC score in supervised settings compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-09-15T08:58:42Z)
• Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning [93.63638405586354]
  We propose a simple and effective meta-training framework, called pseudo-labeling based meta-learning (PLML) Firstly, we train a classifier via common semi-supervised learning (SSL) and use it to obtain the pseudo-labels of unlabeled data. We build few-shot tasks from labeled and pseudo-labeled data and design a novel finetuning method with feature smoothing and noise suppression.
  arXiv  Detail & Related papers  (2022-07-14T10:53:53Z)
• A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning [54.617688468341704]
  Few-shot learning aims to learn models that generalize to novel classes with limited training samples. We propose a novel paradigm containing two parts: (1) a well-designed meta-training algorithm for mitigating ambiguity between base and novel classes caused by unreliable pseudo labels and (2) a model adaptation mechanism to learn discriminative features for novel classes while preserving base knowledge using few labeled and all the unlabeled data.
  arXiv  Detail & Related papers  (2021-10-21T13:25:52Z)
• SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning [5.5616364225463055]
  We introduce a new framework, Subsetting features of Tabular data (SubTab) In this paper, we introduce a new framework, Subsetting features of Tabular data (SubTab) We argue that reconstructing the data from the subset of its features rather than its corrupted version in an autoencoder setting can better capture its underlying representation.
  arXiv  Detail & Related papers  (2021-10-08T20:11:09Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.