18,318 research outputs found
Statistical Physics and Representations in Real and Artificial Neural Networks
This document presents the material of two lectures on statistical physics
and neural representations, delivered by one of us (R.M.) at the Fundamental
Problems in Statistical Physics XIV summer school in July 2017. In a first
part, we consider the neural representations of space (maps) in the
hippocampus. We introduce an extension of the Hopfield model, able to store
multiple spatial maps as continuous, finite-dimensional attractors. The phase
diagram and dynamical properties of the model are analyzed. We then show how
spatial representations can be dynamically decoded using an effective Ising
model capturing the correlation structure in the neural data, and compare
applications to data obtained from hippocampal multi-electrode recordings and
by (sub)sampling our attractor model. In a second part, we focus on the problem
of learning data representations in machine learning, in particular with
artificial neural networks. We start by introducing data representations
through some illustrations. We then analyze two important algorithms, Principal
Component Analysis and Restricted Boltzmann Machines, with tools from
statistical physics
Active Discovery of Network Roles for Predicting the Classes of Network Nodes
Nodes in real world networks often have class labels, or underlying
attributes, that are related to the way in which they connect to other nodes.
Sometimes this relationship is simple, for instance nodes of the same class are
may be more likely to be connected. In other cases, however, this is not true,
and the way that nodes link in a network exhibits a different, more complex
relationship to their attributes. Here, we consider networks in which we know
how the nodes are connected, but we do not know the class labels of the nodes
or how class labels relate to the network links. We wish to identify the best
subset of nodes to label in order to learn this relationship between node
attributes and network links. We can then use this discovered relationship to
accurately predict the class labels of the rest of the network nodes.
We present a model that identifies groups of nodes with similar link
patterns, which we call network roles, using a generative blockmodel. The model
then predicts labels by learning the mapping from network roles to class labels
using a maximum margin classifier. We choose a subset of nodes to label
according to an iterative margin-based active learning strategy. By integrating
the discovery of network roles with the classifier optimisation, the active
learning process can adapt the network roles to better represent the network
for node classification. We demonstrate the model by exploring a selection of
real world networks, including a marine food web and a network of English
words. We show that, in contrast to other network classifiers, this model
achieves good classification accuracy for a range of networks with different
relationships between class labels and network links
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Latent Embeddings for Collective Activity Recognition
Rather than simply recognizing the action of a person individually,
collective activity recognition aims to find out what a group of people is
acting in a collective scene. Previ- ous state-of-the-art methods using
hand-crafted potentials in conventional graphical model which can only define a
limited range of relations. Thus, the complex structural de- pendencies among
individuals involved in a collective sce- nario cannot be fully modeled. In
this paper, we overcome these limitations by embedding latent variables into
feature space and learning the feature mapping functions in a deep learning
framework. The embeddings of latent variables build a global relation
containing person-group interac- tions and richer contextual information by
jointly modeling broader range of individuals. Besides, we assemble atten- tion
mechanism during embedding for achieving more com- pact representations. We
evaluate our method on three col- lective activity datasets, where we
contribute a much larger dataset in this work. The proposed model has achieved
clearly better performance as compared to the state-of-the- art methods in our
experiments.Comment: 6pages, accepted by IEEE-AVSS201
The role of emotional variables in the classification and prediction of collective social dynamics
We demonstrate the power of data mining techniques for the analysis of
collective social dynamics within British Tweets during the Olympic Games 2012.
The classification accuracy of online activities related to the successes of
British athletes significantly improved when emotional components of tweets
were taken into account, but employing emotional variables for activity
prediction decreased the classifiers' quality. The approach could be easily
adopted for any prediction or classification study with a set of
problem-specific variables.Comment: 16 pages, 9 figures, 2 tables and 1 appendi
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
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