41,682 research outputs found
Self Organized Dynamic Tree Neural Network
Cluster analysis is a technique used in a variety of fields. There are currently various algorithms used for grouping elements that are based on different methods including partitional, hierarchical, density studies, probabilistic, etc. This article will present the SODTNN, which can perform clustering by integrating hierarchical and density-based methods. The network incorporates the behavior of self-organizing maps and does not specify the number of existing clusters in order to create the various groups
Automated construction of a hierarchy of self-organized neural network classifiers
This paper documents an effort to design and implement a neural network-based, automatic classification system which dynamically constructs and trains a decision tree. The system is a combination of neural network and decision tree technology. The decision tree is constructed to partition a large classification problem into smaller problems. The neural network modules then solve these smaller problems. We used a variant of the Fuzzy ARTMAP neural network which can be trained much more quickly than traditional neural networks. The research extends the concept of self-organization from within the neural network to the overall structure of the dynamically constructed decision hierarchy. The primary advantage is avoidance of manual tedium and subjective bias in constructing decision hierarchies. Additionally, removing the need for manual construction of the hierarchy opens up a large class of potential classification applications. When tested on data from real-world images, the automatically generated hierarchies performed slightly better than an intuitive (handbuilt) hierarchy. Because the neural networks at the nodes of the decision hierarchy are solving smaller problems, generalization performance can really be improved if the number of features used to solve these problems is reduced. Algorithms for automatically selecting which features to use for each individual classification module were also implemented. We were able to achieve the same level of performance as in previous manual efforts, but in an efficient, automatic manner. The technology developed has great potential in a number of commercial areas, including data mining, pattern recognition, and intelligent interfaces for personal computer applications. Sample applications include: fraud detection, bankruptcy prediction, data mining agent, scalable object recognition system, email agent, resource librarian agent, and a decision aid agent
Progressive growing of self-organized hierarchical representations for exploration
Designing agent that can autonomously discover and learn a diversity of
structures and skills in unknown changing environments is key for lifelong
machine learning. A central challenge is how to learn incrementally
representations in order to progressively build a map of the discovered
structures and re-use it to further explore. To address this challenge, we
identify and target several key functionalities. First, we aim to build lasting
representations and avoid catastrophic forgetting throughout the exploration
process. Secondly we aim to learn a diversity of representations allowing to
discover a "diversity of diversity" of structures (and associated skills) in
complex high-dimensional environments. Thirdly, we target representations that
can structure the agent discoveries in a coarse-to-fine manner. Finally, we
target the reuse of such representations to drive exploration toward an
"interesting" type of diversity, for instance leveraging human guidance.
Current approaches in state representation learning rely generally on
monolithic architectures which do not enable all these functionalities.
Therefore, we present a novel technique to progressively construct a Hierarchy
of Observation Latent Models for Exploration Stratification, called HOLMES.
This technique couples the use of a dynamic modular model architecture for
representation learning with intrinsically-motivated goal exploration processes
(IMGEPs). The paper shows results in the domain of automated discovery of
diverse self-organized patterns, considering as testbed the experimental
framework from Reinke et al. (2019)
Homeostatic plasticity and external input shape neural network dynamics
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.
Structure and control of self-sustained target waves in excitable small-world networks
Small-world networks describe many important practical systems among which
neural networks consisting of excitable nodes are the most typical ones. In
this paper we study self-sustained oscillations of target waves in excitable
small-world networks. A novel dominant phase-advanced driving (DPAD) method,
which is generally applicable for analyzing all oscillatory complex networks
consisting of nonoscillatory nodes, is proposed to reveal the self-organized
structures supporting this type of oscillations. The DPAD method explicitly
explores the oscillation sources and wave propagation paths of the systems,
which are otherwise deeply hidden in the complicated patterns of randomly
distributed target groups. Based on the understanding of the self-organized
structure, the oscillatory patterns can be controlled with extremely high
efficiency.Comment: 16 pages 5 figure
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An improved connectionist activation function for energy minimization
Symmetric networks that are based on energy minimization, such as Boltzmann machines or Hopfield nets, are used extensively for optimization, constraint satisfaction, and approximation of NP-hard problems. Nevertheless, finding a global minimum for the energy function is not guaranteed, and even a local minimum may take an exponential number of steps. We propose an improvement to the standard activation function used for such networks. The improved algorithm guarantees that a global minimum is found in linear time for tree-like subnetworks. The algorithm is uniform and does not assume that the network is a tree. It performs no worse than the standard algorithms for any network topology. In the case where there are trees growing from a cyclic subnetwork, the new algorithm performs better than the standard algorithms by avoiding local minima along the trees and by optimizing the free energy of these trees in linear time. The algorithm is self-stabilizing for trees (cycle-free undirected graphs) and remains correct under various scheduling demons. However, no uniform protocol exists to optimize trees under a pure distributed demon and no such protocol exists for cyclic networks under central demon
Contextualized Non-local Neural Networks for Sequence Learning
Recently, a large number of neural mechanisms and models have been proposed
for sequence learning, of which self-attention, as exemplified by the
Transformer model, and graph neural networks (GNNs) have attracted much
attention. In this paper, we propose an approach that combines and draws on the
complementary strengths of these two methods. Specifically, we propose
contextualized non-local neural networks (CN), which can both
dynamically construct a task-specific structure of a sentence and leverage rich
local dependencies within a particular neighborhood.
Experimental results on ten NLP tasks in text classification, semantic
matching, and sequence labeling show that our proposed model outperforms
competitive baselines and discovers task-specific dependency structures, thus
providing better interpretability to users.Comment: Accepted by AAAI201
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