1,934 research outputs found
Towards Deeper Understanding in Neuroimaging
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in feature discovery, with relevant applications to neuroimaging. Through our works within, this dissertation presents strong evidence that deep learning is a viable and important tool for neuroimaging studies
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
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two probabilistic generative models with
binary latent variables using a novel variational EM approach. The approach
distinguishes itself from previous variational approaches by using latent
states as variational parameters. Here we use efficient and general purpose
sampling procedures to vary the latent states, and investigate the "black box"
applicability of the resulting optimization procedure. For general purpose
applicability, samples are drawn from approximate marginal distributions of the
considered generative model as well as from the model's prior distribution. As
such, variational sampling is defined in a generic form, and is directly
executable for a given model. As a proof of concept, we then apply the novel
procedure (A) to Binary Sparse Coding (a model with continuous observables),
and (B) to basic Sigmoid Belief Networks (which are models with binary
observables). Numerical experiments verify that the investigated approach
efficiently as well as effectively increases a variational free energy
objective without requiring any additional analytical steps
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