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Found 7 papers, 0 papers with code
Challenges for BBVI with Normalizing Flows
Current black-box variational inference (BBVI) methods require the user to make numerous design choices---such as the selection of variational objective and approximating family---yet there is little principled guidance on how to do so.
Challenges and Opportunities in High Dimensional Variational Inference
Our framework and supporting experiments help to distinguish between the behavior of BBVI methods for approximating low-dimensional versus moderate-to-high-dimensional posteriors.
Challenges and Opportunities in High-dimensional Variational Inference
Our framework and supporting experiments help to distinguish between the behavior of BBVI methods for approximating low-dimensional versus moderate-to-high-dimensional posteriors.
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations using stochastic optimization methods.
Preferential Batch Bayesian Optimization
This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems.
Semi-supervised Learning with Sparse Autoencoders in Phone Classification
We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works.
Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
We present a systematic analysis on the performance of a phonetic recogniser when the window of input features is not symmetric with respect to the current frame.
