2,635 research outputs found
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
Symbolic regression of generative network models
Networks are a powerful abstraction with applicability to a variety of
scientific fields. Models explaining their morphology and growth processes
permit a wide range of phenomena to be more systematically analysed and
understood. At the same time, creating such models is often challenging and
requires insights that may be counter-intuitive. Yet there currently exists no
general method to arrive at better models. We have developed an approach to
automatically detect realistic decentralised network growth models from
empirical data, employing a machine learning technique inspired by natural
selection and defining a unified formalism to describe such models as computer
programs. As the proposed method is completely general and does not assume any
pre-existing models, it can be applied "out of the box" to any given network.
To validate our approach empirically, we systematically rediscover pre-defined
growth laws underlying several canonical network generation models and credible
laws for diverse real-world networks. We were able to find programs that are
simple enough to lead to an actual understanding of the mechanisms proposed,
namely for a simple brain and a social network
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
A reversible infinite HMM using normalised random measures
We present a nonparametric prior over reversible Markov chains. We use
completely random measures, specifically gamma processes, to construct a
countably infinite graph with weighted edges. By enforcing symmetry to make the
edges undirected we define a prior over random walks on graphs that results in
a reversible Markov chain. The resulting prior over infinite transition
matrices is closely related to the hierarchical Dirichlet process but enforces
reversibility. A reinforcement scheme has recently been proposed with similar
properties, but the de Finetti measure is not well characterised. We take the
alternative approach of explicitly constructing the mixing measure, which
allows more straightforward and efficient inference at the cost of no longer
having a closed form predictive distribution. We use our process to construct a
reversible infinite HMM which we apply to two real datasets, one from
epigenomics and one ion channel recording.Comment: 9 pages, 6 figure
Computational studies of genome evolution and regulation
This thesis takes on the challenge of extracting information from large volumes of biological data produced with newly established experimental techniques. The different types of information present in a particular dataset have been carefully identified to maximise the information gained from the data. This also precludes the attempts to infer the types of information that are not present in the data. In the first part of the thesis I examined the evolutionary origins of de novo taxonomically restricted genes (TRGs) in Drosophila subgenus. De novo TRGs are genes that have originated after the speciation of a particular clade from previously non-coding regions - functional ncRNA, within introns or alternative frames of older protein-coding genes, or from intergenic sequences. TRGs are clade-specific tool-kits that are likely to contain proteins with yet undocumented functions and new protein folds that are yet to be discovered. One of the main challenges in studying de novo TRGs is the trade-off between false positives (non-functional open reading frames) and false negatives (true TRGs that have properties distinct from well established genes). Here I identified two de novo TRG families in Drosophila subgenus that have not been previously reported as de novo originated genes, and to our knowledge they are the best candidates identified so far for experimental studies aimed at elucidating the properties of de novo genes. In the second part of the thesis I examined the information contained in single cell RNA sequencing (scRNA-seq) data and propose a method for extracting biological knowledge from this data using generative neural networks. The main challenge is the noisiness of scRNA-seq data - the number of transcripts sequenced is not proportional to the number of mRNAs present in the cell. I used an autoencoder to reduce the dimensionality of the data without making untestable assumptions about the data. This embedding into lower dimensional space alongside the features learned by an autoencoder contains information about the cell populations, differentiation trajectories and the regulatory relationships between the genes. Unlike most methods currently used, an autoencoder does not assume that these regulatory relationships are the same in all cells in the data set. The main advantages of our approach is that it makes minimal assumptions about the data, it is robust to noise and it is possible to assess its performance. In the final part of the thesis I summarise lessons learnt from analysing various types of biological data and make suggestions for the future direction of similar computational studies
Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in large
vision models and reinforcement learning (RL) tasks. But to obtain
well-calibrated uncertainty estimates, a grid-search over the dropout
probabilities is necessary - a prohibitive operation with large models, and an
impossible one with RL. We propose a new dropout variant which gives improved
performance and better calibrated uncertainties. Relying on recent developments
in Bayesian deep learning, we use a continuous relaxation of dropout's discrete
masks. Together with a principled optimisation objective, this allows for
automatic tuning of the dropout probability in large models, and as a result
faster experimentation cycles. In RL this allows the agent to adapt its
uncertainty dynamically as more data is observed. We analyse the proposed
variant extensively on a range of tasks, and give insights into common practice
in the field where larger dropout probabilities are often used in deeper model
layers
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