2018/10/18 by Simone Rossi, Rossi, Simone, Pietro Michiardi +3 · 2 citations
Computer Science · Mathematics · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.08083
8 pages of main paper (+3 for references and +6 of supplement material)
openalex publication_date 2018/10/18 · arxiv created 2019/01/25 · arxiv updated 2019/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializations for loss minimization in deep learning, far less attention has been devoted to the issue of initialization of stochastic variational inference. We address this by proposing a novel layer-wise initialization strategy based on Bayesian linear models. The proposed method is extensively validated on regression and classification tasks, including Bayesian DeepNets and ConvNets, showing faster and better convergence compared to alternatives inspired by the literature on initializations for loss minimization.