Feature Engineering
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Benchmarks
2019_test set
Most implemented
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Wide & Deep Learning for Recommender Systems
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Deep & Cross Network for Ad Click Predictions
Named Entity Recognition with Bidirectional LSTM-CNNs
Papers
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, task descriptions, and auxiliary files that carry dataset semantics. Me…
Feature EngineeringTabFM: A Zero-Shot Foundation Model for Tabular Data
Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We present TabFM, a 400M-parameter tabular foundation model that formulat…
Feature EngineeringQuantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers
Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produc…
Feature EngineeringState of Health Estimation using Convolutional and Bidirectional LSTM Neural Networks tuned by Bayesian Optimization
In this research, a novel framework is proposed for the SOH estimation, which employs a hybrid deep learning architecture of a concatenation of a Convolution Neural Network (CNN) and a Bidirectional Long Short-Term Memor…
Feature EngineeringSymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data
Tabular data, as a core data format in machine learning, often lacks the discriminative power needed for high-performance modeling due to insufficient feature informativeness. Automated Feature Engineering (AutoFE) overc…
Feature EngineeringFuture Querying: Can LLMs Serve as Implicit Medical World Models?
Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured text. To address this, we introduce future querying, a paradigm that p…
Feature Engineering