W97-1002 - ACL Anthology Reference Corpus
... make use of limited syntactic and semantic information, using freely available, robust knowledge sources such as a part-of-speech tagger and a lexicon with semantic classes, such as the hypernym links in WordNet (Miller et al., 1993). The initial implementation does not use a parser, primarily becau ...
... make use of limited syntactic and semantic information, using freely available, robust knowledge sources such as a part-of-speech tagger and a lexicon with semantic classes, such as the hypernym links in WordNet (Miller et al., 1993). The initial implementation does not use a parser, primarily becau ...
Robotics Presentation
... Algorithm starts by partitioning input points into k initial sets, either at random or using some heuristic data. Then it calculates the mean point, or centroid, of each set. Constructs new partition by associating each point with the closest centroid. Recalculates centroids for new clusters Repeats ...
... Algorithm starts by partitioning input points into k initial sets, either at random or using some heuristic data. Then it calculates the mean point, or centroid, of each set. Constructs new partition by associating each point with the closest centroid. Recalculates centroids for new clusters Repeats ...
Picture 2.12. Some of the more often used neuron`s
... A neuron presented in this picture is the most typical “material”, which is used for creating a network. More precisely – such typical “material” is a neuron of a network defined as MLP (Multi–Layer Perceptron), the most crucial elements of which I have collected and presented in picture 2.14. It is ...
... A neuron presented in this picture is the most typical “material”, which is used for creating a network. More precisely – such typical “material” is a neuron of a network defined as MLP (Multi–Layer Perceptron), the most crucial elements of which I have collected and presented in picture 2.14. It is ...
Hopfield Networks - liacs
... • Hebb’s learning rule: – Make connection stronger if neurons have the same state – Make connection weaker if the neurons have a different state ...
... • Hebb’s learning rule: – Make connection stronger if neurons have the same state – Make connection weaker if the neurons have a different state ...
AAAI Proceedings Template - Department of Communication and
... only nodes and links (Franklin 2005). However, their use is impractical to represent the kind of processed information produced by complex perception. For example, representing a detailed visual image with nodes and links does not appear to be the most effective representation from which to make geo ...
... only nodes and links (Franklin 2005). However, their use is impractical to represent the kind of processed information produced by complex perception. For example, representing a detailed visual image with nodes and links does not appear to be the most effective representation from which to make geo ...
presentation
... SIMULATION RESULTS FOR NETWORK 2 NEURONS 7 AND 8 First, neurons 7 and 8 are unsynchronized, then we enable the astrocytes To inject slow inward currents EPSPs ...
... SIMULATION RESULTS FOR NETWORK 2 NEURONS 7 AND 8 First, neurons 7 and 8 are unsynchronized, then we enable the astrocytes To inject slow inward currents EPSPs ...
Classification of Artificial Intelligence IDS
... learning contest, in which the learning task was to build a predictive model to differentiate attacks and normal connections. Contestants trained and tested their classifiers on an intrusion data set provided by MIT labs. This was based on features of the KDDCUP 99 training and testing data. Sung et ...
... learning contest, in which the learning task was to build a predictive model to differentiate attacks and normal connections. Contestants trained and tested their classifiers on an intrusion data set provided by MIT labs. This was based on features of the KDDCUP 99 training and testing data. Sung et ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... After the network was trained it was tested for level 2 testing with a new set of data. The dataset taken was of 100 plus records. The new confusion matrix showed a success rate of 54.5% and error rate of 45.5%. Then we tested for Level 3 with a new set of data. The new confusion matrix showed a suc ...
... After the network was trained it was tested for level 2 testing with a new set of data. The dataset taken was of 100 plus records. The new confusion matrix showed a success rate of 54.5% and error rate of 45.5%. Then we tested for Level 3 with a new set of data. The new confusion matrix showed a suc ...
Presentation
... • Bi : state abstraction function which maps state s in the original MDP into an abstract state in Mi • Ai : The set of subtasks that can be called by Mi • Gi : Termination predicate ...
... • Bi : state abstraction function which maps state s in the original MDP into an abstract state in Mi • Ai : The set of subtasks that can be called by Mi • Gi : Termination predicate ...
ALGORITHMICS - Universitatea de Vest din Timisoara
... The complex behavior emerges from simple rules which interact and are applied in parallel This bottom-up approach is opposite to the top down approach particular to classical artificial intelligence The learning ability derives from the adaptability of some parameters associated with the proce ...
... The complex behavior emerges from simple rules which interact and are applied in parallel This bottom-up approach is opposite to the top down approach particular to classical artificial intelligence The learning ability derives from the adaptability of some parameters associated with the proce ...
The Schizophrenic Brain: A Broken Hermeneutic
... in the attractor structure, that may implement such positive symptoms, as hallucinations and delusion. More analyses are needed to relate impairment of global (interregional) and local (intraregional) connections to the emergence of schizophrenia. Nonlinear theories of schizophrenia have been sugges ...
... in the attractor structure, that may implement such positive symptoms, as hallucinations and delusion. More analyses are needed to relate impairment of global (interregional) and local (intraregional) connections to the emergence of schizophrenia. Nonlinear theories of schizophrenia have been sugges ...
STDP produces robust oscillatory architectures that exhibit precise
... stimulus that was repeatedly applied. In this latter work a synchronous response gradually emerges, and the synchrony becomes sharp as learning proceeds. The authors state that the generation of synchrony itself does not depend on the length of the cycle of external input, however they found that sy ...
... stimulus that was repeatedly applied. In this latter work a synchronous response gradually emerges, and the synchrony becomes sharp as learning proceeds. The authors state that the generation of synchrony itself does not depend on the length of the cycle of external input, however they found that sy ...
bioresources.com - NC State University
... and the artificial neural network model to the same sample. In order to be able to realize these predictions, the financial margins of the companies were utilized, and as a result of the prediction study on 142 companies, results regarded as successful were obtained from all three of the developed m ...
... and the artificial neural network model to the same sample. In order to be able to realize these predictions, the financial margins of the companies were utilized, and as a result of the prediction study on 142 companies, results regarded as successful were obtained from all three of the developed m ...
Artificial neural networks and their application in biological and
... means that a given neuron sums up input signals with appropriate weight values obtained from prior neurons and creates a non-linear threshold function of the sum obtained, which is sent as input signal to other connected neurons. The rule functioning in ANN functions is based on an “all” or “nothing ...
... means that a given neuron sums up input signals with appropriate weight values obtained from prior neurons and creates a non-linear threshold function of the sum obtained, which is sent as input signal to other connected neurons. The rule functioning in ANN functions is based on an “all” or “nothing ...
Machine Learning
... – If the output is too high, lower the weights proportional to the values of their corresponding features, so the overall output decreases – If the output is too low, increase the weights proportional to the values of their corresponding features, so the overall output increases. ...
... – If the output is too high, lower the weights proportional to the values of their corresponding features, so the overall output decreases – If the output is too low, increase the weights proportional to the values of their corresponding features, so the overall output increases. ...
Solutions of the BCM learning rule in a network of lateral interacting
... functions between the set of three-input vectors and the three-output neurons. The associative solutions can be divided into completely associative and partially associative, where completely associative refers to those solutions that associate all the neurons to a single input pattern. It is clear ...
... functions between the set of three-input vectors and the three-output neurons. The associative solutions can be divided into completely associative and partially associative, where completely associative refers to those solutions that associate all the neurons to a single input pattern. It is clear ...
Why Probability?
... unobserved variables given observed variables • Approximation finds distribution in family with simpler functional form (e.g., remove some arcs in graph) by minimizing a measure of distance from ...
... unobserved variables given observed variables • Approximation finds distribution in family with simpler functional form (e.g., remove some arcs in graph) by minimizing a measure of distance from ...
Artificial Intelligence Chapter 7 - Computer Science
... • It is a well-known and interesting psychological phenomenon that if a cold stimulus is applied to a person’s skin for a short period of time, the person will perceive heat. • However, if the same stimulus is applied for a longer period of time, the person will perceive cold. The use of discrete ti ...
... • It is a well-known and interesting psychological phenomenon that if a cold stimulus is applied to a person’s skin for a short period of time, the person will perceive heat. • However, if the same stimulus is applied for a longer period of time, the person will perceive cold. The use of discrete ti ...
Modeling Human-Level Intelligence by Integrated - CEUR
... is obvious: whereas symbolic theories are based on recursion and compositionality allowing the computation of (potentially) infinitely many meanings from a finite basis, such principles are not available for connectionist networks. On the other hand, neural networks have been proven to be a robust t ...
... is obvious: whereas symbolic theories are based on recursion and compositionality allowing the computation of (potentially) infinitely many meanings from a finite basis, such principles are not available for connectionist networks. On the other hand, neural networks have been proven to be a robust t ...
Artificial Neural Networks Introduction to connectionism
... 2. Items to be categorized as separate classes should be given widely different representations in the NN. 3. If a particular feature is important, then there should be a large number of neurons involved in the representation of that item in the NN. 4. Prior information and invariances should be bui ...
... 2. Items to be categorized as separate classes should be given widely different representations in the NN. 3. If a particular feature is important, then there should be a large number of neurons involved in the representation of that item in the NN. 4. Prior information and invariances should be bui ...
A Beginner`s Guide to the Mathematics of Neural Networks
... `trained'. They gradually `learn' to perform tasks by being presented with examples of what they are supposed to do. The key question then is to understand the relationships between the network performance for a given type of task, the choice of `learning rule' (the recipe for the modi cation of the ...
... `trained'. They gradually `learn' to perform tasks by being presented with examples of what they are supposed to do. The key question then is to understand the relationships between the network performance for a given type of task, the choice of `learning rule' (the recipe for the modi cation of the ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.