TabM

NeuroTabModels.Models.TabM.EnsembleView Type

EnsembleView(k)

Repeat (D, B) input to (D, K, B). Passes through (D, K, B) unchanged.

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NeuroTabModels.Models.TabM.LinearBatchEnsemble Type

LinearBatchEnsemble(in_f, out_f; k, scaling_init=:random_signs,
                    first_scaling_init_chunks=nothing, bias=true)

Batch-ensemble linear: y = S ⊙ (W(R ⊙ x)) + bias.

Arguments

• in_f, out_f: Input and output dimensions.

• k::Int: Ensemble size.

• scaling_init: :ones, :normal, or :random_signs; or (R, S) tuple.

• first_scaling_init_chunks: Grouped init for input scaling R.

• bias::Bool: Per-member bias (default true).

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NeuroTabModels.Models.TabM.LinearEnsemble Type

LinearEnsemble(in_f, out_f, k; bias=true)

k independent linear layers via batched_matmul. Input/output (features, k, batch).

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NeuroTabModels.Models.TabM.ScaleEnsemble Type

ScaleEnsemble(k, d; init=:random_signs, init_chunks=nothing, bias=false)

Per-member elementwise scaling on (d, k, batch) input.

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NeuroTabModels.Models.TabM.TabMConfig Type

TabMConfig(; kwargs...)

Configuration for TabM ensemble backbones.

Arguments

• k::Int: Ensemble size (default 32).

• n_blocks::Int: Number of MLP blocks (default 3).

• d_block::Int: Hidden dimension (default 512).

• dropout::Float64: Dropout rate (default 0.1).

• arch_type::Symbol: :tabm, :tabm_mini, or :tabm_packed (default :tabm).

• scaling_init::Union{Nothing,Symbol}: :random_signs, :normal, :ones, or nothing (default nothing).

• MLE_tree_split::Bool: Split output head for Gaussian MLE (default false).

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NeuroTabModels.Models.TabM.TabMConfig Method

(config::TabMConfig)(; nfeats, outsize, d_features=nothing, scaling_init_override=nothing)

Build a Lux.Chain from config. Output shape is (outsize, k, batch).

Arguments

• nfeats::Int: Number of input features.

• outsize::Int: Number of output units.

• d_features: Per-feature sizes for grouped scaling init (default ones(Int, nfeats)).

• scaling_init_override: Used when config.scaling_init is nothing.

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