Experimental Models

Warning

Experimental tier: These models are not covered by DeepTab’s stable-model API guarantees. Pin the exact DeepTab version when using them in reproducible studies or production-like workflows.

Experimental models are research-facing architectures that are available for evaluation before they graduate to the stable model zoo. They are useful for benchmarking new inductive biases, studying architectural behavior, and contributing empirical evidence back to DeepTab.

Available Models

Model	Core Idea	Best Research Use	Main Cost Driver
ModernNCA	Differentiable nearest-neighbor prediction in a learned representation space	Testing whether local similarity structure helps a dataset	Pairwise distance to candidate rows
Tangos	MLP with gradient-attribution specialization and orthogonalization penalties	Studying regularization of dense tabular networks	Jacobian computation during training
Trompt	Prompt-style recurrent tabular representation cells	Evaluating prompt-inspired tabular architectures	Prompt-feature importance maps over cycles

Quick Usage

from deeptab.configs import ModernNCAConfig, TangosConfig, TrainerConfig, TromptConfig
from deeptab.models.experimental import ModernNCAClassifier, TangosClassifier, TromptClassifier

trainer_cfg = TrainerConfig(max_epochs=100, batch_size=128, lr=3e-4, patience=15)

modern_nca = ModernNCAClassifier(
    model_config=ModernNCAConfig(dim=128, n_blocks=4, temperature=0.75),
    trainer_config=trainer_cfg,
)

tangos = TangosClassifier(
    model_config=TangosConfig(layer_sizes=[256, 128, 32], lamda1=0.5, lamda2=0.1),
    trainer_config=trainer_cfg,
)

trompt = TromptClassifier(
    model_config=TromptConfig(d_model=128, n_cycles=6, n_cells=4, P=128),
    trainer_config=trainer_cfg,
)

Selection Guidance

If your research question is…	Start with	Compare against
Does a learned local-neighbor rule beat parametric prediction?	ModernNCA	TabR, TabM, ResNet
Can attribution-based regularization improve a plain MLP?	Tangos	MLP, ResNet, TabM
Do prompt-style latent records help tabular feature aggregation?	Trompt	FTTransformer, Mambular, TabM
Do I need a reliable model for production today?	Stable model zoo	Mambular, TabM, ResNet, FTTransformer

Important

When benchmarking an experimental model, include at least one tuned simple baseline such as MLP, ResNet, or TabM. Otherwise it is hard to tell whether the experimental mechanism adds value beyond optimization and preprocessing.

Stability Roadmap

Experimental models are candidates for stable promotion when they show:

Competitive performance under a declared search budget.
Reliable convergence across datasets and random seeds.
Clear configuration defaults and failure modes.
Documentation that explains both architecture and implementation details.
Community feedback from real use cases.

See Model Promotion Policy for the promotion criteria.