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. Deep neural networks remain a highly competitive and more efficient modeling paradigm when equipped with well-designed architectures; however, identifying such architectures in a data-adaptive and budget-aware manner remains challenging. We propose pTNAS, the first progressive neural architecture search (NAS) approach tailored for tabular data, which enables f
The continuous evolution of AI models demands more efficient and adaptable architectures, particularly for established data types like tabular data, where foundation models are showing high inference costs.
This development allows for more resource-efficient and adaptable AI models for tabular data, critical for diverse industry applications where computational budget is a constraint.
The ability to dynamically search for optimal neural network architectures tailored for tabular data will lead to more accurate and cost-effective AI solutions compared to prior approaches.
- · AI developers
- · Enterprises with large tabular datasets
- · Cloud computing providers (through optimized resource use)
- · Inefficient general-purpose AI models
- · Organizations relying on manual model architecture design
Improved performance and reduced costs for AI applications using tabular data across various sectors.
Accelerated adoption of deep learning for tasks previously considered suboptimal due to architectural challenges and inference costs.
Enhanced competitive landscape in specific AI solutions as custom, high-performing models become more accessible to a wider range of developers.
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Read at arXiv cs.LG