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Transformation Autoregressive Networks

ICML 2018 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Likelihood-free and graphical model estimation · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Combine flexible transformations with autoregressive conditionalsTAN density estimation · POWER, GAS, HEPMASS, MINIBOONE, BSDS300average test log-likelihood: POWER: 0.24 ± 0.01 -> 0.60 ± 0.01; GAS: 10.08 ± 0.02 -> 12.06 ± 0.02; HEPMASS: -15.15 ± 0.02 -> -13.78 ± 0.02; MINIBOONEHelped
Add a linear transformation before LAM + 4xAdd+Re (ablation)LAM + 4xAdd+Re · Star 128dheld-out test log-likelihood: 77.195 -> 88.837Helped
Add a linear transformation before LAM + L RNN (ablation)LAM autoregressive density estimator · Markovian syntheticheld-out test log-likelihood: 14.319 -> 15.658Helped
Add 4xSRNN+Re and a linear transformation (ablation)RAM autoregressive density estimator · Markovian syntheticheld-out test log-likelihood: -29.950 -> 16.202Helped
Add L 4xSRNN+Re transformations (ablation)LAM autoregressive density estimator · Star 32dheld-out test log-likelihood: -2.041 -> 16.042Helped
Replace LAM conditionals with independent mixture conditionals (ablation)L RNN transformation on Markovian synthetic · Markovian syntheticheld-out test log-likelihood: 15.658 -> 5.370Hurt
Replace RAM conditionals with independent mixture conditionals (ablation)L RNN+4xAdd+Re transformation · forestheld-out test log-likelihood: 2.672 -> 0.450Hurt
Remove the initial linear transformation (ablation)LAM + L 4xAdd+Re · Star 128dheld-out test log-likelihood: 88.837 -> 77.195Hurt
Choose a single transformation and conditional combination across datasets (ablation)TAN · all ablation datasetsaverage performance percentage score: L RNN with RAM: 0.540; L RNN+4xAdd+Re with LAM: 0.336Mixed
Use TAN density scores for unsupervised anomaly detectionTAN · forest, pendigits, satimage2average precisionHelped

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