107,912 research outputs found
Learning Graph Representation via Formal Concept Analysis
We present a novel method that can learn a graph representation from
multivariate data. In our representation, each node represents a cluster of
data points and each edge represents the subset-superset relationship between
clusters, which can be mutually overlapped. The key to our method is to use
formal concept analysis (FCA), which can extract hierarchical relationships
between clusters based on the algebraic closedness property. We empirically
show that our method can effectively extract hierarchical structures of
clusters compared to the baseline method.Comment: 5 pages, 2 figures, Relational Representation Learning Workshop
(NeurIPS 2018
Semantically Enhanced Models for Commonsense Knowledge Acquisition
Commonsense knowledge is paramount to enable intelligent systems. Typically,
it is characterized as being implicit and ambiguous, hindering thereby the
automation of its acquisition. To address these challenges, this paper presents
semantically enhanced models to enable reasoning through resolving part of
commonsense ambiguity. The proposed models enhance in a knowledge graph
embedding (KGE) framework for knowledge base completion. Experimental results
show the effectiveness of the new semantic models in commonsense reasoning
Toward a Formal Model of Cognitive Synergy
"Cognitive synergy" refers to a dynamic in which multiple cognitive
processes, cooperating to control the same cognitive system, assist each other
in overcoming bottlenecks encountered during their internal processing.
Cognitive synergy has been posited as a key feature of real-world general
intelligence, and has been used explicitly in the design of the OpenCog
cognitive architecture. Here category theory and related concepts are used to
give a formalization of the cognitive synergy concept.
A series of formal models of intelligent agents is proposed, with increasing
specificity and complexity: simple reinforcement learning agents; "cognit"
agents with an abstract memory and processing model; hypergraph-based agents
(in which "cognit" operations are carried out via hypergraphs); hypergraph
agents with a rich language of nodes and hyperlinks (such as the OpenCog
framework provides); "PGMC" agents whose rich hypergraphs are endowed with
cognitive processes guided via Probabilistic Growth and Mining of Combinations;
and finally variations of the PrimeAGI design, which is currently being built
on top of OpenCog.
A notion of cognitive synergy is developed for cognitive processes acting
within PGMC agents, based on developing a formal notion of "stuckness," and
defining synergy as a relationship between cognitive processes in which they
can help each other out when they get stuck. It is proposed that cognitive
processes relating to each other synergetically, associate in a certain way
with functors that map into each other via natural transformations. Cognitive
synergy is proposed to correspond to a certain inequality regarding the
relative costs of different paths through certain commutation diagrams.
Applications of this notion of cognitive synergy to particular cognitive
phenomena, and specific cognitive processes in the PrimeAGI design, are
discussed
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Despite its great success, machine learning can have its limits when dealing
with insufficient training data. A potential solution is the additional
integration of prior knowledge into the training process which leads to the
notion of informed machine learning. In this paper, we present a structured
overview of various approaches in this field. We provide a definition and
propose a concept for informed machine learning which illustrates its building
blocks and distinguishes it from conventional machine learning. We introduce a
taxonomy that serves as a classification framework for informed machine
learning approaches. It considers the source of knowledge, its representation,
and its integration into the machine learning pipeline. Based on this taxonomy,
we survey related research and describe how different knowledge representations
such as algebraic equations, logic rules, or simulation results can be used in
learning systems. This evaluation of numerous papers on the basis of our
taxonomy uncovers key methods in the field of informed machine learning.Comment: Accepted at IEEE Transactions on Knowledge and Data Engineering:
https://ieeexplore.ieee.org/document/942998
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Deep convolutional networks have witnessed unprecedented success in various
machine learning applications. Formal understanding on what makes these
networks so successful is gradually unfolding, but for the most part there are
still significant mysteries to unravel. The inductive bias, which reflects
prior knowledge embedded in the network architecture, is one of them. In this
work, we establish a fundamental connection between the fields of quantum
physics and deep learning. We use this connection for asserting novel
theoretical observations regarding the role that the number of channels in each
layer of the convolutional network fulfills in the overall inductive bias.
Specifically, we show an equivalence between the function realized by a deep
convolutional arithmetic circuit (ConvAC) and a quantum many-body wave
function, which relies on their common underlying tensorial structure. This
facilitates the use of quantum entanglement measures as well-defined
quantifiers of a deep network's expressive ability to model intricate
correlation structures of its inputs. Most importantly, the construction of a
deep ConvAC in terms of a Tensor Network is made available. This description
enables us to carry a graph-theoretic analysis of a convolutional network, with
which we demonstrate a direct control over the inductive bias of the deep
network via its channel numbers, that are related to the min-cut in the
underlying graph. This result is relevant to any practitioner designing a
network for a specific task. We theoretically analyze ConvACs, and empirically
validate our findings on more common ConvNets which involve ReLU activations
and max pooling. Beyond the results described above, the description of a deep
convolutional network in well-defined graph-theoretic tools and the formal
connection to quantum entanglement, are two interdisciplinary bridges that are
brought forth by this work
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
Collaborative ontology sharing and editing
This article first lists reasons why - in the long term or when creating a
new knowledge base (KB) for general knowledge sharing purposes -
collaboratively building a well-organized KB does/can provide more
possibilities, with on the whole no more costs, than the mainstream approach
where knowledge creation and re-use involves searching, merging and creating
(semi-)independent (relatively small) ontologies or semi-formal documents. The
article lists elements required to achieve this and describes the main one: a
KB editing protocol that keeps the KB free of automatically/manually detected
inconsistencies while not forcing them to discuss or agree on terminology and
beliefs nor requiring a selection committee.Comment: 12 pages, 2 figures, journa
Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics
Ontology Learning (OL) is the computational task of generating a knowledge
base in the form of an ontology given an unstructured corpus whose content is
in natural language (NL). Several works can be found in this area most of which
are limited to statistical and lexico-syntactic pattern matching based
techniques Light-Weight OL. These techniques do not lead to very accurate
learning mostly because of several linguistic nuances in NL. Formal OL is an
alternative (less explored) methodology were deep linguistics analysis is made
using theory and tools found in computational linguistics to generate formal
axioms and definitions instead simply inducing a taxonomy. In this paper we
propose "Description Logic (DL)" based formal OL framework for learning factual
IS-A type sentences in English. We claim that semantic construction of IS-A
sentences is non trivial. Hence, we also claim that such sentences requires
special studies in the context of OL before any truly formal OL can be
proposed. We introduce a learner tool, called DLOL_IS-A, that generated such
ontologies in the owl format. We have adopted "Gold Standard" based OL
evaluation on IS-A rich WCL v.1.1 dataset and our own Community representative
IS-A dataset. We observed significant improvement of DLOL_IS-A when compared to
the light-weight OL tool Text2Onto and formal OL tool FRED.Comment: This paper has been withdrawn by the author due to requirement of
re-evaluation of result
RHOG: A Refinement-Operator Library for Directed Labeled Graphs
This document provides the foundations behind the functionality provided by
the G library (https://github.com/santiontanon/RHOG), focusing on the
basic operations the library provides: subsumption, refinement of directed
labeled graphs, and distance/similarity assessment between directed labeled
graphs. G development was initially supported by the National Science
Foundation, by the EAGER grant IIS-1551338.Comment: Report of the theory behind the RHOG library developed under NSF
EAGER grant IIS-155133
Semantics, Representations and Grammars for Deep Learning
Deep learning is currently the subject of intensive study. However,
fundamental concepts such as representations are not formally defined --
researchers "know them when they see them" -- and there is no common language
for describing and analyzing algorithms. This essay proposes an abstract
framework that identifies the essential features of current practice and may
provide a foundation for future developments.
The backbone of almost all deep learning algorithms is backpropagation, which
is simply a gradient computation distributed over a neural network. The main
ingredients of the framework are thus, unsurprisingly: (i) game theory, to
formalize distributed optimization; and (ii) communication protocols, to track
the flow of zeroth and first-order information. The framework allows natural
definitions of semantics (as the meaning encoded in functions), representations
(as functions whose semantics is chosen to optimized a criterion) and grammars
(as communication protocols equipped with first-order convergence guarantees).
Much of the essay is spent discussing examples taken from the literature. The
ultimate aim is to develop a graphical language for describing the structure of
deep learning algorithms that backgrounds the details of the optimization
procedure and foregrounds how the components interact. Inspiration is taken
from probabilistic graphical models and factor graphs, which capture the
essential structural features of multivariate distributions.Comment: 20 pages, many diagram
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