4,400 research outputs found
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
Structure-Aware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or parameters
can take a long time if one employs sequential procedures even with stochastic
updates. A natural solution is to turn to distributed computing on a cluster;
however, naive, unstructured parallelization of ML algorithms does not usually
lead to a proportional speedup and can even result in divergence, because
dependencies between model elements can attenuate the computational gains from
parallelization and compromise correctness of inference. Recent efforts toward
this issue have benefited from exploiting the static, a priori block structures
residing in ML algorithms. In this paper, we take this path further by
exploring the dynamic block structures and workloads therein present during ML
program execution, which offers new opportunities for improving convergence,
correctness, and load balancing in distributed ML. We propose and showcase a
general-purpose scheduler, STRADS, for coordinating distributed updates in ML
algorithms, which harnesses the aforementioned opportunities in a systematic
way. We provide theoretical guarantees for our scheduler, and demonstrate its
efficacy versus static block structures on Lasso and Matrix Factorization
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
Probabilistic Graphical Model Representation in Phylogenetics
Recent years have seen a rapid expansion of the model space explored in
statistical phylogenetics, emphasizing the need for new approaches to
statistical model representation and software development. Clear communication
and representation of the chosen model is crucial for: (1) reproducibility of
an analysis, (2) model development and (3) software design. Moreover, a
unified, clear and understandable framework for model representation lowers the
barrier for beginners and non-specialists to grasp complex phylogenetic models,
including their assumptions and parameter/variable dependencies.
Graphical modeling is a unifying framework that has gained in popularity in
the statistical literature in recent years. The core idea is to break complex
models into conditionally independent distributions. The strength lies in the
comprehensibility, flexibility, and adaptability of this formalism, and the
large body of computational work based on it. Graphical models are well-suited
to teach statistical models, to facilitate communication among phylogeneticists
and in the development of generic software for simulation and statistical
inference.
Here, we provide an introduction to graphical models for phylogeneticists and
extend the standard graphical model representation to the realm of
phylogenetics. We introduce a new graphical model component, tree plates, to
capture the changing structure of the subgraph corresponding to a phylogenetic
tree. We describe a range of phylogenetic models using the graphical model
framework and introduce modules to simplify the representation of standard
components in large and complex models. Phylogenetic model graphs can be
readily used in simulation, maximum likelihood inference, and Bayesian
inference using, for example, Metropolis-Hastings or Gibbs sampling of the
posterior distribution
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