25,235 research outputs found
Iterative Amortized Inference
Inference models are a key component in scaling variational inference to deep
latent variable models, most notably as encoder networks in variational
auto-encoders (VAEs). By replacing conventional optimization-based inference
with a learned model, inference is amortized over data examples and therefore
more computationally efficient. However, standard inference models are
restricted to direct mappings from data to approximate posterior estimates. The
failure of these models to reach fully optimized approximate posterior
estimates results in an amortization gap. We aim toward closing this gap by
proposing iterative inference models, which learn to perform inference
optimization through repeatedly encoding gradients. Our approach generalizes
standard inference models in VAEs and provides insight into several empirical
findings, including top-down inference techniques. We demonstrate the inference
optimization capabilities of iterative inference models and show that they
outperform standard inference models on several benchmark data sets of images
and text.Comment: International Conference on Machine Learning (ICML) 201
Deep Generative Models for Reject Inference in Credit Scoring
Credit scoring models based on accepted applications may be biased and their
consequences can have a statistical and economic impact. Reject inference is
the process of attempting to infer the creditworthiness status of the rejected
applications. In this research, we use deep generative models to develop two
new semi-supervised Bayesian models for reject inference in credit scoring, in
which we model the data generating process to be dependent on a Gaussian
mixture. The goal is to improve the classification accuracy in credit scoring
models by adding reject applications. Our proposed models infer the unknown
creditworthiness of the rejected applications by exact enumeration of the two
possible outcomes of the loan (default or non-default). The efficient
stochastic gradient optimization technique used in deep generative models makes
our models suitable for large data sets. Finally, the experiments in this
research show that our proposed models perform better than classical and
alternative machine learning models for reject inference in credit scoring
- …