{"total":44,"rows":[{"id":"2106.11959","title":"Revisiting Deep Learning Models for Tabular Data","created":"2021-06-22","cited_by_count":740,"area":null},{"id":"2106.03253","title":"Tabular Data: Deep Learning is Not All You Need","created":"2021-06-06","cited_by_count":535,"area":null},{"id":"2203.05556","title":"On Embeddings for Numerical Features in Tabular Deep Learning","created":"2022-03-10","cited_by_count":186,"area":null},{"id":"2410.24210","title":"TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling","created":"2024-10-31","cited_by_count":113,"area":null},{"id":"2405.09321","title":"ReconBoost: Boosting Can Achieve Modality Reconcilement","created":"2024-05-15","cited_by_count":60,"area":null},{"id":"2206.15306","title":"Transfer Learning with Deep Tabular Models","created":"2022-06-30","cited_by_count":52,"area":null},{"id":"2407.04057","title":"TALENT: A Tabular Analytics and Learning Toolbox","created":"2024-07-04","cited_by_count":52,"area":null},{"id":"2101.08543","title":"Boost then Convolve: Gradient Boosting Meets Graph Neural Networks","created":"2021-01-21","cited_by_count":44,"area":null},{"id":"2207.03208","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","created":"2022-07-07","cited_by_count":43,"area":null},{"id":"2502.17361","title":"A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities","created":"2025-02-24","cited_by_count":41,"area":null},{"id":"2401.02013","title":"SwitchTab: Switched Autoencoders Are Effective Tabular Learners","created":"2024-01-04","cited_by_count":38,"area":null},{"id":"2502.02527","title":"TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems","created":"2025-02-04","cited_by_count":36,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2304.02858","title":"A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation","created":"2023-04-06","cited_by_count":25,"area":null},{"id":"2310.18541","title":"ReConTab: Regularized Contrastive Representation Learning for Tabular Data","created":"2023-10-28","cited_by_count":25,"area":null},{"id":"2311.05877","title":"A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning","created":"2023-11-10","cited_by_count":24,"area":null},{"id":"2210.13447","title":"Precision Machine Learning","created":"2022-10-24","cited_by_count":14,"area":null},{"id":"2405.07414","title":"Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains","created":"2024-05-13","cited_by_count":12,"area":null},{"id":"2211.16726","title":"Boosted Dynamic Neural Networks","created":"2022-11-30","cited_by_count":8,"area":null},{"id":"2412.10871","title":"Fully Test-time Adaptation for Tabular Data","created":"2024-12-14","cited_by_count":8,"area":null},{"id":"2502.17788","title":"On-device edge learning for IoT data streams: a survey","created":"2025-02-25","cited_by_count":8,"area":null},{"id":"2410.13203","title":"TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering","created":"2024-10-17","cited_by_count":5,"area":null},{"id":"2308.13816","title":"Homological Convolutional Neural Networks","created":"2023-08-26","cited_by_count":4,"area":null},{"id":"2505.05237","title":"Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2505.16226","title":"Realistic Evaluation of TabPFN v2 in Open Environments","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2407.09790","title":"Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs","created":"2024-07-13","cited_by_count":3,"area":null},{"id":"2407.13238","title":"Transformers with Stochastic Competition for Tabular Data Modelling","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2302.11327","title":"A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2402.14789","title":"Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning","created":"2024-02-22","cited_by_count":2,"area":null},{"id":"2411.11148","title":"TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data","created":"2024-11-17","cited_by_count":2,"area":null},{"id":"2206.13116","title":"Transfer learning for ensembles: reducing computation time and keeping the diversity","created":"2022-06-27","cited_by_count":1,"area":null},{"id":"2305.16222","title":"Incomplete Multimodal Learning for Complex Brain Disorders Prediction","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2405.07836","title":"Forecasting with Hyper-Trees","created":"2024-05-13","cited_by_count":1,"area":null},{"id":"2608.17856","title":"ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction","created":"2026-08-18","cited_by_count":1,"area":null},{"id":"2210.09565","title":"Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting","created":"2022-10-18","cited_by_count":0,"area":null},{"id":"2306.00698","title":"Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers","created":"2023-06-01","cited_by_count":0,"area":null},{"id":"2312.07680","title":"I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives","created":"2023-12-12","cited_by_count":0,"area":null},{"id":"2403.09548","title":"Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2502.10095","title":"Representation Learning on Out of Distribution in Tabular Data","created":"2025-02-14","cited_by_count":0,"area":null},{"id":"2505.05744","title":"Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification","created":"2025-05-09","cited_by_count":0,"area":null},{"id":"2506.15064","title":"HiPreNets: High-Precision Neural Networks through Progressive Training","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2603.20634","title":"CFNN: Continued Fraction Neural Network","created":"2026-03-21","cited_by_count":0,"area":null},{"id":"2604.03190","title":"Gradient Boosting within a Single Attention Layer","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2608.18026","title":"TabNSM: Neural Sparse Mixer for Tabular Regression","created":"2026-08-18","cited_by_count":0,"area":null}],"next_cursor":null}