1,077 research outputs found
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Intelligent systems based on first-order logic on the one hand, and on
artificial neural networks (also called connectionist systems) on the other,
differ substantially. It would be very desirable to combine the robust neural
networking machinery with symbolic knowledge representation and reasoning
paradigms like logic programming in such a way that the strengths of either
paradigm will be retained. Current state-of-the-art research, however, fails by
far to achieve this ultimate goal. As one of the main obstacles to be overcome
we perceive the question how symbolic knowledge can be encoded by means of
connectionist systems: Satisfactory answers to this will naturally lead the way
to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page
Provably Stable Interpretable Encodings of Context Free Grammars in RNNs with a Differentiable Stack
Given a collection of strings belonging to a context free grammar (CFG) and
another collection of strings not belonging to the CFG, how might one infer the
grammar? This is the problem of grammatical inference. Since CFGs are the
languages recognized by pushdown automata (PDA), it suffices to determine the
state transition rules and stack action rules of the corresponding PDA. An
approach would be to train a recurrent neural network (RNN) to classify the
sample data and attempt to extract these PDA rules. But neural networks are not
a priori aware of the structure of a PDA and would likely require many samples
to infer this structure. Furthermore, extracting the PDA rules from the RNN is
nontrivial. We build a RNN specifically structured like a PDA, where weights
correspond directly to the PDA rules. This requires a stack architecture that
is somehow differentiable (to enable gradient-based learning) and stable (an
unstable stack will show deteriorating performance with longer strings). We
propose a stack architecture that is differentiable and that provably exhibits
orbital stability. Using this stack, we construct a neural network that
provably approximates a PDA for strings of arbitrary length. Moreover, our
model and method of proof can easily be generalized to other state machines,
such as a Turing Machine.Comment: 20 pages, 2 figure
Incremental construction of LSTM recurrent neural network
Long Short--Term Memory (LSTM) is a recurrent neural network that
uses structures called memory blocks to allow the net remember
significant events distant in the past input sequence in order to
solve long time lag tasks, where other RNN approaches fail.
Throughout this work we have performed experiments using LSTM
networks extended with growing abilities, which we call GLSTM.
Four methods of training growing LSTM has been compared. These
methods include cascade and fully connected hidden layers as well
as two different levels of freezing previous weights in the
cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five
controllers of the Central Nervous System control has to be
modelled. We have compared growing LSTM results against other
neural networks approaches, and our work applying conventional
LSTM to the task at hand.Postprint (published version
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