Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
Fuente:
arXiv
Saved in:
| Main Authors: | Rabbani, Shourav B., Medri, Ivan V., Samad, Manar D. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023)
by: Rabbani, Shourav B., et al.
Published: (2023)
Transfer Learning of Tabular Data by Finetuning Large Language Models
by: Rabbani, Shourav B., et al.
Published: (2025)
by: Rabbani, Shourav B., et al.
Published: (2025)
Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
by: Samad, Manar D., et al.
Published: (2025)
by: Samad, Manar D., et al.
Published: (2025)
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework
by: Kowsar, Ibna, et al.
Published: (2025)
by: Kowsar, Ibna, et al.
Published: (2025)
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
by: Hou, Yina, et al.
Published: (2025)
by: Hou, Yina, et al.
Published: (2025)
LATTLE: LLM Attention Transplant for Transfer Learning of Tabular Data Across Disparate Domains
by: Kowsar, Ibna, et al.
Published: (2025)
by: Kowsar, Ibna, et al.
Published: (2025)
Predicting Early and Complete Drug Release from Long-Acting Injectables Using Explainable Machine Learning
by: Robles, Karla N., et al.
Published: (2026)
by: Robles, Karla N., et al.
Published: (2026)
Continual Contrastive Learning on Tabular Data with Out of Distribution
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, et al.
Published: (2025)
Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
by: Samad, Manar D., et al.
Published: (2026)
by: Samad, Manar D., et al.
Published: (2026)
Unveiling the Role of Data Uncertainty in Tabular Deep Learning
by: Kartashev, Nikolay, et al.
Published: (2025)
by: Kartashev, Nikolay, et al.
Published: (2025)
Contrastive Federated Learning with Tabular Data Silos
by: Ginanjar, Achmad, et al.
Published: (2024)
by: Ginanjar, Achmad, et al.
Published: (2024)
TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
by: Eslamian, Ali, et al.
Published: (2025)
by: Eslamian, Ali, et al.
Published: (2025)
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
by: Liu, Zhining, et al.
Published: (2025)
by: Liu, Zhining, et al.
Published: (2025)
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
by: Margraf, Valentin, et al.
Published: (2024)
by: Margraf, Valentin, et al.
Published: (2024)
TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes
by: Liu, Ruizhe, et al.
Published: (2026)
by: Liu, Ruizhe, et al.
Published: (2026)
Benchmarking Optimizers for MLPs in Tabular Deep Learning
by: Gorishniy, Yury, et al.
Published: (2026)
by: Gorishniy, Yury, et al.
Published: (2026)
TabArena: A Living Benchmark for Machine Learning on Tabular Data
by: Erickson, Nick, et al.
Published: (2025)
by: Erickson, Nick, et al.
Published: (2025)
Benchmarking Distribution Shift in Tabular Data with TableShift
by: Gardner, Josh, et al.
Published: (2023)
by: Gardner, Josh, et al.
Published: (2023)
Benchmarking Federated Machine Unlearning methods for Tabular Data
by: Xiao, Chenguang, et al.
Published: (2025)
by: Xiao, Chenguang, et al.
Published: (2025)
Towards Benchmarking Foundation Models for Tabular Data With Text
by: Mráz, Martin, et al.
Published: (2025)
by: Mráz, Martin, et al.
Published: (2025)
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
by: Vogel, Liane, et al.
Published: (2026)
by: Vogel, Liane, et al.
Published: (2026)
Attention-Based Deep Learning for Early Parkinson's Disease Detection with Tabular Biomedical Data
by: Oseni, Olamide Samuel, et al.
Published: (2026)
by: Oseni, Olamide Samuel, et al.
Published: (2026)
FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
by: Tayebi, Aida, et al.
Published: (2025)
by: Tayebi, Aida, et al.
Published: (2025)
Random Client Selection on Contrastive Federated Learning for Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, et al.
Published: (2025)
On Learning Representations for Tabular Data Distillation
by: Kang, Inwon, et al.
Published: (2025)
by: Kang, Inwon, et al.
Published: (2025)
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
by: Herurkar, Dayananda, et al.
Published: (2025)
by: Herurkar, Dayananda, et al.
Published: (2025)
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
by: Lindskog, William, et al.
Published: (2024)
by: Lindskog, William, et al.
Published: (2024)
Data-centric Graph Learning: A Survey
by: Guo, Yuxin, et al.
Published: (2023)
by: Guo, Yuxin, et al.
Published: (2023)
A Systematic Framework for Tabular Data Disentanglement
by: Tjuawinata, Ivan, et al.
Published: (2026)
by: Tjuawinata, Ivan, et al.
Published: (2026)
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)
TabDDPM: Modelling Tabular Data with Diffusion Models
by: Kotelnikov, Akim, et al.
Published: (2022)
by: Kotelnikov, Akim, et al.
Published: (2022)
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
by: Poeta, Eleonora, et al.
Published: (2024)
by: Poeta, Eleonora, et al.
Published: (2024)
Representation Learning for Tabular Data: A Comprehensive Survey
by: Jiang, Jun-Peng, et al.
Published: (2025)
by: Jiang, Jun-Peng, et al.
Published: (2025)
Multi-branch of Attention Yields Accurate Results for Tabular Data
by: Li, Xuechen, et al.
Published: (2025)
by: Li, Xuechen, et al.
Published: (2025)
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
by: Chen, Suiyao, et al.
Published: (2024)
by: Chen, Suiyao, 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)
Representation Learning on Out of Distribution in Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, et al.
Published: (2025)
Contextual Learning for Anomaly Detection in Tabular Data
by: King, Spencer, et al.
Published: (2025)
by: King, Spencer, et al.
Published: (2025)
Algorithmic Recourse of In-Context Learning for Tabular Data
by: Dong, Wenshuo, et al.
Published: (2026)
by: Dong, Wenshuo, et al.
Published: (2026)
Transformers for Tabular Data: A Training Perspective of Self-Attention via Optimal Transport
by: Quadrio, Alessandro, et al.
Published: (2025)
by: Quadrio, Alessandro, et al.
Published: (2025)
Similar Items
-
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
by: Rabbani, Shourav B., et al.
Published: (2023) -
Transfer Learning of Tabular Data by Finetuning Large Language Models
by: Rabbani, Shourav B., et al.
Published: (2025) -
Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer
by: Samad, Manar D., et al.
Published: (2025) -
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework
by: Kowsar, Ibna, et al.
Published: (2025) -
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
by: Hou, Yina, et al.
Published: (2025)