arXiv AI
Aug 11
iLTM: Integrated Large Tabular Model
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.
pTNAS: Progressive Neural Architecture Search for Tabular Data
arXiv:2403. 10318v3 Announce Type: replace Abstract: Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context size.
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arXiv Machine Learning
Aug 4
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
arXiv:2608. 01400v1 Announce Type: new Abstract: Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity.
arXiv AI
Aug 12
TACTICL: Task-Aware Compression of Tabular ICL Models
arXiv:2608. 10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs.
arXiv AI
2d ago
LP-NAS: Linear Programming-based Neural Architecture Search
arXiv:2608. 14472v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise.
arXiv Machine Learning
Jun 8
TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
arXiv:2606. 07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples.
arXiv Machine Learning
Jul 7
TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
arXiv:2607. 05380v1 Announce Type: new Abstract: In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures.
arXiv Machine Learning
Jul 31
Memory Efficient Tabular Foundation Models
arXiv:2607. 27546v1 Announce Type: new Abstract: Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often exceeds classical baselines.
arXiv AI
Jun 19
PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units
arXiv:2603. 15106v2 Announce Type: replace Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be specialized for each device separately.
arXiv AI
Jun 24
SEAL: Searching Expandable Architectures for Incremental Learning
arXiv:2505. 10457v3 Announce Type: replace-cross Abstract: Incremental learning is a machine learning paradigm where a model learns from a sequential stream of tasks.
arXiv Machine Learning
Jul 20
Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
arXiv:2607. 15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches.
arXiv Machine Learning
Jul 14
TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data
arXiv:2607. 10077v1 Announce Type: new Abstract: Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive.
arXiv Machine Learning
Jun 2
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.