T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data
Fuente:
arXiv
Saved in:
| Main Authors: | Thimonier, Hugo, Costa, José Lucas De Melo, Popineau, Fabrice, Rimmel, Arpad, Doan, Bich-Liên |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Retrieval Augmented Deep Anomaly Detection for Tabular Data
by: Thimonier, Hugo, et al.
Published: (2024)
by: Thimonier, Hugo, et al.
Published: (2024)
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
by: Thimonier, Hugo, et al.
Published: (2023)
by: Thimonier, Hugo, et al.
Published: (2023)
High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models
by: Costa, José Lucas De Melo, et al.
Published: (2026)
by: Costa, José Lucas De Melo, et al.
Published: (2026)
TracInAD: Measuring Influence for Anomaly Detection
by: Thimonier, Hugo, et al.
Published: (2022)
by: Thimonier, Hugo, et al.
Published: (2022)
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
by: Velay, Marc, et al.
Published: (2023)
by: Velay, Marc, et al.
Published: (2023)
KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning
by: Zimmermann, Eric, et al.
Published: (2025)
by: Zimmermann, Eric, et al.
Published: (2025)
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
by: Wang, Wei-Yao, et al.
Published: (2024)
by: Wang, Wei-Yao, et al.
Published: (2024)
Self-Supervision Improves Diffusion Models for Tabular Data Imputation
by: Liu, Yixin, et al.
Published: (2024)
by: Liu, Yixin, et al.
Published: (2024)
LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
by: Balestriero, Randall, et al.
Published: (2025)
by: Balestriero, Randall, et al.
Published: (2025)
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
by: Muhammad, Hada Melino, et al.
Published: (2026)
by: Muhammad, Hada Melino, et al.
Published: (2026)
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning
by: Feigin, Shlomo Libo, et al.
Published: (2024)
by: Feigin, Shlomo Libo, et al.
Published: (2024)
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
by: Li, Jiayu, et al.
Published: (2024)
by: Li, Jiayu, et al.
Published: (2024)
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?
by: Gowda, Shruthi, et al.
Published: (2024)
by: Gowda, Shruthi, et al.
Published: (2024)
Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture -- Bridging Predictive and Generative Self-Supervised Learning
by: Gögl, Moritz, et al.
Published: (2026)
by: Gögl, Moritz, et al.
Published: (2026)
EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition
by: Thimonier, Hugo, et al.
Published: (2025)
by: Thimonier, Hugo, et al.
Published: (2025)
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
by: Lee, Kyungeun, et al.
Published: (2024)
by: Lee, Kyungeun, et al.
Published: (2024)
An Augmentation-Aware Theory for Self-Supervised Contrastive Learning
by: Cui, Jingyi, et al.
Published: (2025)
by: Cui, Jingyi, et al.
Published: (2025)
SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography
by: Del Pup, Federico, et al.
Published: (2023)
by: Del Pup, Federico, et al.
Published: (2023)
FeatNavigator: Automatic Feature Augmentation on Tabular Data
by: Liang, Jiaming, et al.
Published: (2024)
by: Liang, Jiaming, et al.
Published: (2024)
Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression
by: Wang, Yung-Chien, et al.
Published: (2025)
by: Wang, Yung-Chien, et al.
Published: (2025)
Deep Learning with Tabular Data: A Self-supervised Approach
by: Vyas, Tirth Kiranbhai
Published: (2024)
by: Vyas, Tirth Kiranbhai
Published: (2024)
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
by: Assran, Mido, et al.
Published: (2025)
by: Assran, Mido, et al.
Published: (2025)
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data
by: Zhang, Xiaochen, et al.
Published: (2025)
by: Zhang, Xiaochen, et al.
Published: (2025)
Semantic Tube Prediction: Beating LLM Data Efficiency with JEPA
by: Huang, Hai, et al.
Published: (2026)
by: Huang, Hai, et al.
Published: (2026)
CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision
by: Luo, Xuanhao, et al.
Published: (2026)
by: Luo, Xuanhao, et al.
Published: (2026)
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)
by: Bühler, Magnus, et al.
Published: (2026)
Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
by: Marin, Javier
Published: (2022)
by: Marin, Javier
Published: (2022)
MATATA: Weakly Supervised End-to-End MAthematical Tool-Augmented Reasoning for Tabular Applications
by: Vinayagame, Vishnou, et al.
Published: (2024)
by: Vinayagame, Vishnou, et al.
Published: (2024)
On Learning Representations for Tabular Data Distillation
by: Kang, Inwon, et al.
Published: (2025)
by: Kang, Inwon, et al.
Published: (2025)
Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning
by: Brüel-Gabrielsson, Rickard, et al.
Published: (2023)
by: Brüel-Gabrielsson, Rickard, et al.
Published: (2023)
Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
by: Cui, Lingxi, et al.
Published: (2024)
by: Cui, Lingxi, et al.
Published: (2024)
How Well Does Your Tabular Generator Learn the Structure of Tabular Data?
by: Jiang, Xiangjian, et al.
Published: (2025)
by: Jiang, Xiangjian, et al.
Published: (2025)
On the Importance of Embedding Norms in Self-Supervised Learning
by: Draganov, Andrew, et al.
Published: (2025)
by: Draganov, Andrew, et al.
Published: (2025)
Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework
by: Luo, Shiyuan, et al.
Published: (2025)
by: Luo, Shiyuan, et al.
Published: (2025)
Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation
by: Cui, Wei, et al.
Published: (2024)
by: Cui, Wei, et al.
Published: (2024)
Learning from Anonymized and Incomplete Tabular Data
by: Lange, Lucas, et al.
Published: (2026)
by: Lange, Lucas, et al.
Published: (2026)
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
by: Stocksieker, Samuel, et al.
Published: (2024)
by: Stocksieker, Samuel, et al.
Published: (2024)
Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset
by: Hwang, Yoontae, et al.
Published: (2024)
by: Hwang, Yoontae, et al.
Published: (2024)
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
by: Wei, Jack Yi, et al.
Published: (2026)
by: Wei, Jack Yi, et al.
Published: (2026)
Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
by: Ye, Han-Jia, et al.
Published: (2024)
by: Ye, Han-Jia, et al.
Published: (2024)
Similar Items
-
Retrieval Augmented Deep Anomaly Detection for Tabular Data
by: Thimonier, Hugo, et al.
Published: (2024) -
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
by: Thimonier, Hugo, et al.
Published: (2023) -
High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models
by: Costa, José Lucas De Melo, et al.
Published: (2026) -
TracInAD: Measuring Influence for Anomaly Detection
by: Thimonier, Hugo, et al.
Published: (2022) -
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
by: Velay, Marc, et al.
Published: (2023)