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Prior Labs Unveils TabPFN-2.5: Advancing Tabular Foundation Models with Enhanced Scale and Speed

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RUST-BENCH: Unveiling LLM Weaknesses in Real-World Table Reasoning

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Enhancing LLM Numerical Reasoning in Tables with a Decompose-Sanitize-Reason Framework

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Orion-MSP: Advancing Tabular Data Learning with Smart Attention and Memory

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Enhancing Malware Detection with Synthetic Tabular Data Framework

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MPRU: A New Approach to Machine Unlearning Through Output Filtering

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Aeolus: A New Multi-Modal Dataset for Understanding Flight Delays

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Unpacking AI’s Ability to Translate Tables into Natural Language

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Unlocking Tabular Data Utility: CA-GAN Preserves Cause-Effect Relationships in Synthetic Datasets

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Unlocking Richer Spatial Analysis with the World-POI Dataset

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Unmasking Latent Knowledge: How LLMs ‘Remember’ Tabular Data Meanings, Not Entries

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Time-Conditioned Contraction Matching: A New Era for Scalable and Explainable Anomaly Detection

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A New Framework for Responsible AI Evaluation: Understanding RAISE

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Unlocking Hidden Biases: A Causal Approach to AI Fairness Testing

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Boosting Imbalanced Classification with Diverse Synthetic Data from Large Language Models

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Unlocking Causal Relationships in Tabular Data with CALM: A New Language Model Approach

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Ship Fuel Consumption Prediction: A New Benchmark and Foundation Model Evaluation

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Enhancing Neural Networks with Complex Number Data Transformations

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Enhancing Anomaly Detection with Semantic Context in Tabular Data

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TABINR: A Neural Approach to Filling Gaps in Tabular Data

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Assessing LLM Reliability in Tabular Feature Engineering: A Multi-level Approach

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Evaluating Large Language Model Configurations for Synthetic Tabular Data Generation

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TABFAIRGDT: Generating Fair Tabular Data with Efficient Decision Trees

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New Approach Tackles Highly Imbalanced Data for Critical Predictions

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GEM-T: A Maximum Entropy Approach for Generating High-Quality Tabular Data

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Enhancing Salary Prediction: New Methods for Word Embedding Dimensionality Reduction in Tabular Data

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Unmasking Privacy Risks in Synthetic Tabular Data: A New Attack Method Revealed

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Enhancing Tabular Data Quality with AI-Powered Rule and Code Generation

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New AI Framework Improves Prediction of EV Crash Outcomes

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Unveiling Causal Importance: A New Approach to Evaluating Explainable AI for Boolean Logic

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