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Classification on Australian (UCI) (test)

0.343NLLDeepEns(M=5)
0.093641.776823.465.14318Jun 12, 2023Dec 6, 2023Jun 1, 2024Nov 26, 2024May 23, 2025Nov 17, 2025May 14, 2026

Evaluation results

MethodLinks
DeepEns(M=5)Inference cost=multi-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.343
Ordinal-V3Inference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.349
LaplaceInference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.35
Ordinal-V2Inference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.351
CE-softmaxInference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.353
OvA-V2Inference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.355
OvA-V3Inference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.361
VBLLInference cost=single-pass, Backbone=single hidden layer width H=50, tanh activations, Seeds=202026.050.409
RIEM-LAPrior precision=optimized, Number of MC samples=302023.060.541
LINEARIZED LAPrior precision=optimized, Number of MC samples=302023.060.57
VANILLA LAPrior precision=optimized, Number of MC samples=302023.060.684
RLANumber of samples (S)=30, Number of seeds=5, backbone=3-layer network, hidden units=16, activations=SiLU, dropout=50%2026.050.712
LINLANumber of samples (S)=30, Number of seeds=5, backbone=3-layer network, hidden units=16, activations=SiLU, dropout=50%2026.050.789
LINEARIZED RIEM-LAPrior precision=optimized, Number of MC samples=302023.060.833
DIMSNumber of samples (S)=30, Number of seeds=5, backbone=3-layer network, hidden units=16, activations=SiLU, dropout=50%2026.051.258
MAPPrior precision=optimized, Number of MC samples=302023.061.306
LANumber of samples (S)=30, Number of seeds=5, backbone=3-layer network, hidden units=16, activations=SiLU, dropout=50%2026.056.577