8,464 research outputs found
Tree Echo State Networks
In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach: First, we discuss the Markovian characterization of reservoir dynamics, extended to the case of tree domains, that is implied by the contractive setting of the TreeESN state transition function. Second, we study two types of state mapping functions to map the tree structured state of TreeESN into a fixed-size feature representation for classification or regression tasks. The critical role of the relation between the choice of the state mapping function and the Markovian characterization of the task is analyzed and experimentally investigated on both artificial and real-world tasks. Finally, experimental results on benchmark and real-world tasks show that the TreeESN approach, in spite of its efficiency, can achieve comparable results with state-of-the-art, although more complex, neural and kernel based models for tree structured data
Network analysis of a corpus of undeciphered Indus civilization inscriptions indicates syntactic organization
Archaeological excavations in the sites of the Indus Valley civilization
(2500-1900 BCE) in Pakistan and northwestern India have unearthed a large
number of artifacts with inscriptions made up of hundreds of distinct signs. To
date there is no generally accepted decipherment of these sign sequences and
there have been suggestions that the signs could be non-linguistic. Here we
apply complex network analysis techniques to a database of available Indus
inscriptions, with the aim of detecting patterns indicative of syntactic
organization. Our results show the presence of patterns, e.g., recursive
structures in the segmentation trees of the sequences, that suggest the
existence of a grammar underlying these inscriptions.Comment: 17 pages (includes 4 page appendix containing Indus sign list), 14
figure
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Efficient Beam Tree Recursion
Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as a
simple extension of Gumbel Tree RvNN and it was shown to achieve
state-of-the-art length generalization performance in ListOps while maintaining
comparable performance on other tasks. However, although not the worst in its
kind, BT-RvNN can be still exorbitantly expensive in memory usage. In this
paper, we identify the main bottleneck in BT-RvNN's memory usage to be the
entanglement of the scorer function and the recursive cell function. We propose
strategies to remove this bottleneck and further simplify its memory usage.
Overall, our strategies not only reduce the memory usage of BT-RvNN by
- times but also create a new state-of-the-art in ListOps while
maintaining similar performance in other tasks. In addition, we also propose a
strategy to utilize the induced latent-tree node representations produced by
BT-RvNN to turn BT-RvNN from a sentence encoder of the form into a sequence contextualizer of the
form . Thus, our
proposals not only open up a path for further scalability of RvNNs but also
standardize a way to use BT-RvNNs as another building block in the deep
learning toolkit that can be easily stacked or interfaced with other popular
models such as Transformers and Structured State Space models
Approximation contexts in addressing graph data structures
While the application of machine learning algorithms to practical problems has been expanded from fixed sized input data to sequences, trees or graphs input data, the composition of learning system has developed from a single model to integrated ones. Recent advances in graph based learning algorithms include: the SOMSD (Self Organizing Map for Structured Data), PMGraphSOM (Probability Measure Graph Self Organizing Map,GNN (Graph Neural Network) and GLSVM (Graph Laplacian Support Vector Machine). A main motivation of this thesis is to investigate if such algorithms, whether by themselves individually or modified, or in various combinations, would provide better performance over the more traditional artificial neural networks or kernel machine methods on some practical challenging problems. More succinctly, this thesis seeks to answer the main research question: when or under what conditions/contexts could graph based models be adjusted and tailored to be most efficacious in terms of predictive or classification performance on some challenging practical problems? There emerges a range of sub-questions including: how do we craft an effective neural learning system which can be an integration of several graph and non-graph based models? Integration of various graph based and non graph based kernel machine algorithms; enhancing the capability of the integrated model in working with challenging problems; tackling the problem of long term dependency issues which aggravate the performance of layer-wise graph based neural systems. This thesis will answer these questions.
Recent research on multiple staged learning models has demonstrated the efficacy of multiple layers of alternating unsupervised and supervised learning approaches. This underlies the very successful front-end feature extraction techniques in deep neural networks. However much exploration is still possible with the investigation of the number of layers required, and the types of unsupervised or supervised learning models which should be used. Such issues have not been considered so far, when the underlying input data structure is in the form of a graph. We will explore empirically the capabilities of models of increasing complexities, the combination of the unsupervised learning algorithms, SOM, or PMGraphSOM, with or without a cascade connection with a multilayer perceptron, and with or without being followed by multiple layers of GNN. Such studies explore the effects of including or ignoring context. A parallel study involving kernel machines with or without graph inputs has also been conducted empirically
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Proceedings of ECAI International Workshop on Neural-Symbolic Learning and reasoning NeSy 2006
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