2 edition of **On the computational complexity of finding a connectionist model"s stable state vectors.** found in the catalog.

On the computational complexity of finding a connectionist model"s stable state vectors.

John Lipscomb

- 356 Want to read
- 19 Currently reading

Published
**1987**
by University of Toronto, Dept. of Computer Science in Toronto
.

Written in English

**Edition Notes**

Thesis (M.Sc.)--University of Toronto, 1987.

The Physical Object | |
---|---|

Pagination | 38 leaves. |

Number of Pages | 38 |

ID Numbers | |

Open Library | OL18448753M |

An unexpected “side-effect” of such models is that their vectors often exhibit compositionality, i.e., addingtwo word-vectors results in a vector that is only a small angle away from the vector of a word representing the semantic composite of the original words, e.g., “man” + “royal” = “king”. This work provides a theoretical. It has been argued that the mental representation resulting from sentence comprehension is not (just) an abstract symbolic structure but a “mental simulation” of the state-of-affairs described by the sentence. We present a particular formalization of this theory and show how it gives rise to quantifications of the amount of syntactic and semantic information conveyed by each word in Cited by:

Automatic speech recognition (ASR) has been one of the biggest and hardest challenges in the field. A large majority of research in this area focuses on widely spoken languages such as English. The problems of automatic Lithuanian speech recognition have attracted little attention so far. Due to complicated language structure and scarcity of data, models proposed for other Author: Laurynas Pipiras, Rytis Maskeliūnas, Robertas Damaševičius. We're upgrading the ACM DL, and would like your input. Please sign up to review new features, functionality and page by:

The tutorial enables attendees to analyze the computational complexity of evolutionary algorithms and other search heuristics in a rigorous way. An overview of the tools and methods developed within the last 15 years is given and practical examples of the application of these analytical methods are presented. computational complexity theory Focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. computational creativity.

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On the Computational Complexity of Finding Stable State Vectors in Connectionist Models (Hopfield Nets). Technical Report /88, Dept. of Computer Science, Univ.

of Toronto, March Google ScholarCited by: {Li} J. Lipscomb "On the Computational Complexity of Finding a Connectionist Model's Stable State of Vectors," Master's Thesis, Dept. of CS, Univ. of Toronto, Google Scholar {Lub} M. Luby "A Simple Parallel Algorithm for the Maximal Independent Set Problem (Extended Abstract)," in Proc.

17th Annual Symp. J. Lipscomb, On the Computational Complexity of Finding a Connectionist Model's Stable State of Vectors, Master's Thesis, Dept. of Comp. Sci., U. of Toronto, October, Google Scholar [LTT]Cited by: The purpose of this paper is twofold: (a) to provide a tutorial introduction to some key concepts from the theory of computational complexity, highlighting their relevance to systems and control theory, and (b) to survey the relatively recent research activity Cited by: The construction of neural networks requires knowledge about both ideal architectures (connectivity) and most efficient weight vectors (computational power).

The fusion of neural network modeling with evolutionary strategies is therefore a natural step towards artificial neurogenetic modeling. Computational Complexity Of Neural Networks: A Survey lower bounds on the complexity of state reachability problems for these models, extending some of.

These algorithms are shown to allow networks having recurrent connections to learn complex tasks requiring the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks.

Suggested Citation:"5 Computational Modeling and Simulation as Enablers for Biological Discovery."National Research Council. Catalyzing Inquiry at the Interface of Computing and gton, DC: The National Academies Press.

doi: / Computational Linguistics and Deep Learning. may be achieved by the implementation of connectionist models of meta-classification models for NLI, achieving state-of Author: Christopher D.

Manning. Connectionist Natural Language Processing: A Status Report. Michael G. Dyer. Computer Science Department. UCLA, Los Angeles, CA Abstract. Natural language processing requires high-level symbolic capabilities, including: (a) the creation and propagation of dynamic bindings, (b) the manipulation of recursive, constituent structures, (c) the acquisition and.

In this book, Marcin Milkowski argues that the mind can be explained computationally because it is itself computational -- whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music.

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior.

Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them. Connectionist models have had a profound impact on theories of language.

While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language by: This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics.

By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating.

Connectionist models, in contrast, abstract away from biophysical details, thereby making it possible to train large-scale models on large amounts of sensory data, allowing cognitively challenging tasks to be solved. Due to their computational simplicity, they are also more amenable to theoretical analysis (Hertz et al., ; Bishop, ).Cited by: The state representation s t encodes the attentional state that served to identify representations in SMS relevant to a t allowing the EHC to produce the resulting state s t+1.

Given s t we can reproduce the activity recorded in the EMS, and, in principle, incorporate multiple steps and contingencies in a policy constituting a specialized.

perspectives describe how computational models can learn to transition from discrete to a bidirectional RNN produces two state vectors for every word w i, signed with the motivation of substantially improving the computational complexity of the algorithms, along with the general quality of word vectors.

Author: Kian Kenyon-Dean. The capacity to search memory for events learned in a particular context stands as one of the most remarkable feats of the human brain. How is memory search accomplished.

First, I review the central ideas investigated by theorists developing models of memory. Then, I review select benchmark findings concerning memory search and analyze two influential computational. Some open problems related to the computational complexity of control questions are proposed in Blondel, Sontag, Vidyasagar and Willems (c, Probl 14, 22, and 43).

Complexity theory. In this section, we introduce the main concepts from the theory of computability and complexity, with a focus on models of digital computation. Computational complexity theory is the fundamental subject of classifying computational problems based on their `complexities'.

In this context, `complexity' of a problem is a measure of the amount of resource (time/space/random bits, or queries) used by. You can write a book review and share your experiences.

Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.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.The handbook of brain theory and neural networks / Michael A.

Arbib, Spatial Models Hybrid Connectionist/Symbolic Systems Identiﬁcation and Control Neural Automata and Analog Computational Complexity Neuroanatomy in a Computational Perspective Neuroethology, Computational File Size: 31MB.