911 research outputs found
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
A linear approach for sparse coding by a two-layer neural network
Many approaches to transform classification problems from non-linear to
linear by feature transformation have been recently presented in the
literature. These notably include sparse coding methods and deep neural
networks. However, many of these approaches require the repeated application of
a learning process upon the presentation of unseen data input vectors, or else
involve the use of large numbers of parameters and hyper-parameters, which must
be chosen through cross-validation, thus increasing running time dramatically.
In this paper, we propose and experimentally investigate a new approach for the
purpose of overcoming limitations of both kinds. The proposed approach makes
use of a linear auto-associative network (called SCNN) with just one hidden
layer. The combination of this architecture with a specific error function to
be minimized enables one to learn a linear encoder computing a sparse code
which turns out to be as similar as possible to the sparse coding that one
obtains by re-training the neural network. Importantly, the linearity of SCNN
and the choice of the error function allow one to achieve reduced running time
in the learning phase. The proposed architecture is evaluated on the basis of
two standard machine learning tasks. Its performances are compared with those
of recently proposed non-linear auto-associative neural networks. The overall
results suggest that linear encoders can be profitably used to obtain sparse
data representations in the context of machine learning problems, provided that
an appropriate error function is used during the learning phase
Disappearance of Spurious States in Analog Associative Memories
We show that symmetric n-mixture states, when they exist, are almost never
stable in autoassociative networks with threshold-linear units. Only with a
binary coding scheme we could find a limited region of the parameter space in
which either 2-mixtures or 3-mixtures are stable attractors of the dynamics.Comment: 5 pages, 3 figures, accepted for publication in Phys Rev
Microbial life cycles link global modularity in regulation to mosaic evolution
Microbes are exposed to changing environments, to which they can respond by adopting various lifestyles such as swimming, colony formation or dormancy. These lifestyles are often studied in isolation, thereby giving a fragmented view of the life cycle as a whole. Here, we study lifestyles in the context of this whole. We first use machine learning to reconstruct the expression changes underlying life cycle progression in the bacterium Bacillus subtilis, based on hundreds of previously acquired expression profiles. This yields a timeline that reveals the modular organization of the life cycle. By analysing over 380 Bacillales genomes, we then show that life cycle modularity gives rise to mosaic evolution in which life stages such as motility and sporulation are conserved and lost as discrete units. We postulate that this mosaic conservation pattern results from habitat changes that make these life stages obsolete or detrimental. Indeed, when evolving eight distinct Bacillales strains and species under laboratory conditions that favour colony growth, we observe rapid and parallel losses of the sporulation life stage across species, induced by mutations that affect the same global regulator. We conclude that a life cycle perspective is pivotal to understanding the causes and consequences of modularity in both regulation and evolution
- …