Editing Statistical Records by Neural Networks
... The learning functions of the BP algorithm for the two-layer model are generalizations of the single-layer functions described above. They can be presented as ? [j? = o[j?*(1-o[j?)*(t[j?-o[j?) for adjusting the second matrix. and ? [j? = o[j?*(1-o[j?)*?? ?w[j,k?*? [k? for adjusting the first matrix. ...
... The learning functions of the BP algorithm for the two-layer model are generalizations of the single-layer functions described above. They can be presented as ? [j? = o[j?*(1-o[j?)*(t[j?-o[j?) for adjusting the second matrix. and ? [j? = o[j?*(1-o[j?)*?? ?w[j,k?*? [k? for adjusting the first matrix. ...
Aalborg Universitet Nielsen, Jannie Sønderkær; Sørensen, John Dalsgaard
... This example demonstrates how Bayesian networks can be used to update damage size and failure probability, when new information becomes available, and be used for risk-based repair planning. A specific case relevant for wind turbine O&M is considered, but the model is in principle generic, and can e ...
... This example demonstrates how Bayesian networks can be used to update damage size and failure probability, when new information becomes available, and be used for risk-based repair planning. A specific case relevant for wind turbine O&M is considered, but the model is in principle generic, and can e ...
The Emergence of Rule-Use: A Dynamic Neural Field Model of... Aaron Buss ()
... processes at work. It is unclear, for instance, how a hierarchical rule structure could be implemented in real-time in a nervous system. Similarly, ties to known changes in neural development have remained largely at the descriptive level. Morton and Munakata (2001) have made attempts to move explan ...
... processes at work. It is unclear, for instance, how a hierarchical rule structure could be implemented in real-time in a nervous system. Similarly, ties to known changes in neural development have remained largely at the descriptive level. Morton and Munakata (2001) have made attempts to move explan ...
Evolutionary Algorithm for Connection Weights in Artificial Neural
... Evolution has been introduced into ANN’s at roughly three different levels: connection weights, architectures, and learning rules. The evolution of connection weights introduces an adaptive and global approach to training, especially in the reinforcement learning and recurrent network learning parad ...
... Evolution has been introduced into ANN’s at roughly three different levels: connection weights, architectures, and learning rules. The evolution of connection weights introduces an adaptive and global approach to training, especially in the reinforcement learning and recurrent network learning parad ...
Learning to Complete Sentences
... We derive our solution to the sentence completion problem based on a linear interpolation of N -gram models, and briefly discuss an instance-based method that provides an alternative approach and baseline for our experiments. In order to solve the sentence completion problem with an N -gram model, we ...
... We derive our solution to the sentence completion problem based on a linear interpolation of N -gram models, and briefly discuss an instance-based method that provides an alternative approach and baseline for our experiments. In order to solve the sentence completion problem with an N -gram model, we ...
Towards an Empirically Grounded Predictive Coding Account of
... signaling a no-go trial followed by an action instead of a withheld action. This paradigm would allow researchers to directly measure error signals; the neurons with greater activation when predictions are violated versus fulfilled would fall into the category of error units. Just such a signal was ...
... signaling a no-go trial followed by an action instead of a withheld action. This paradigm would allow researchers to directly measure error signals; the neurons with greater activation when predictions are violated versus fulfilled would fall into the category of error units. Just such a signal was ...
A Learning Rule for the Emergence of Stable Dynamics and Timing
... neurons did not result in any suprathreshold activity in the other neurons. With training, the learning rule was effective in generating network activity. However, it did not converge to a steady state in which neurons stabilized at their target activity level. Instead, oscillatory behavior was obse ...
... neurons did not result in any suprathreshold activity in the other neurons. With training, the learning rule was effective in generating network activity. However, it did not converge to a steady state in which neurons stabilized at their target activity level. Instead, oscillatory behavior was obse ...
Hemispheric Asymmetry in Visual Perception Arises from Differential Encoding
... layer. A narrow Gaussian PDF is used for the RH autoencoder network (σ = 1.8), and a wide Gaussian PDF for the LH autoencoder network (σ = 18; see Figure 5)1; the variances are chosen as the two extreme cases of denseness/sparseness of the connections in order to examine the qualitative differences ...
... layer. A narrow Gaussian PDF is used for the RH autoencoder network (σ = 1.8), and a wide Gaussian PDF for the LH autoencoder network (σ = 18; see Figure 5)1; the variances are chosen as the two extreme cases of denseness/sparseness of the connections in order to examine the qualitative differences ...
Measuring Time Series` Similarity through Large Singular Features
... by the operation of coarse graining the data using so-called wavelet filters, in the Wavelet Transformation scheme. In this paper, we will demonstrate how the Wavelet Transform resulting from scale-wise decomposing of time series data provides a natural way to obtain scale-wise ranking of events in ...
... by the operation of coarse graining the data using so-called wavelet filters, in the Wavelet Transformation scheme. In this paper, we will demonstrate how the Wavelet Transform resulting from scale-wise decomposing of time series data provides a natural way to obtain scale-wise ranking of events in ...
Solving Predictive Analogy Tasks with Anti-Unification
... a target domain is explained by specifying similarities with a given base domain (Gentner, 1983). In this paper, we will focus on the third class of analogy-making capacities of human intelligence, namely predictive analogies. This type of analogy is in a certain sense at the very heart of analogies ...
... a target domain is explained by specifying similarities with a given base domain (Gentner, 1983). In this paper, we will focus on the third class of analogy-making capacities of human intelligence, namely predictive analogies. This type of analogy is in a certain sense at the very heart of analogies ...
emergence of linguistic features: independent
... would need to be as closely as possible two times larger than the other. Then one could conclude that the sum would be 21+42. Two variables can be independent, for example, if they could be outcomes of two events that have nothing to do with each other, or random signals originating from two differe ...
... would need to be as closely as possible two times larger than the other. Then one could conclude that the sum would be 21+42. Two variables can be independent, for example, if they could be outcomes of two events that have nothing to do with each other, or random signals originating from two differe ...
Robotic tool use and problem solving based on
... features of the objects and on the robot sensorimotor abilities and previous experiences. Inspired by recent advances in AI, we can use these predictions within probabilistic planning algorithms, to achieve a grounding of the planning operators based on the robot sensorimotor knowledge. Through thi ...
... features of the objects and on the robot sensorimotor abilities and previous experiences. Inspired by recent advances in AI, we can use these predictions within probabilistic planning algorithms, to achieve a grounding of the planning operators based on the robot sensorimotor knowledge. Through thi ...
Oscillatory Neural Fields for Globally Optimal Path Planning
... to noise, thereby supporting the feasibility of analog VLSI implementations. The work reported here is related to resistive grid approaches for solving optimization problems (Chua, 1984). Resistive grid approaches may be viewed as "passive" relaxation methods, while the oscillatory neural field is a ...
... to noise, thereby supporting the feasibility of analog VLSI implementations. The work reported here is related to resistive grid approaches for solving optimization problems (Chua, 1984). Resistive grid approaches may be viewed as "passive" relaxation methods, while the oscillatory neural field is a ...
KISHORE Aswathy - School of Computing
... representation’. Accordingly, different features of the object such as shape, texture and colour will be represented in different parts of the brain. Hence, in order to have a complete representation for the object, these individual localised representations have to be bound together to form a globa ...
... representation’. Accordingly, different features of the object such as shape, texture and colour will be represented in different parts of the brain. Hence, in order to have a complete representation for the object, these individual localised representations have to be bound together to form a globa ...
2 Brain and Classical Neural Networks
... the receptors to open up and allow the penetration of ionic current into the post synaptic neuron. The efficacy of the synapse is a parameter specified by the amount of penetrating current per presynaptic spike. 3. The post synaptic potential (PSP) diffuses toward the soma, where all inputs in a short p ...
... the receptors to open up and allow the penetration of ionic current into the post synaptic neuron. The efficacy of the synapse is a parameter specified by the amount of penetrating current per presynaptic spike. 3. The post synaptic potential (PSP) diffuses toward the soma, where all inputs in a short p ...
AutoLeadGuitar: Automatic Generation of Guitar Solo Phrases in the
... Performance was measured by calculating the precision, recall, and f-measure of detection of phrase boundaries, with an exact match required for a 'hit'. The results of our experiments can be seen in Table II. Inspecting the left portion of Table II, we see that the total precision of our model in d ...
... Performance was measured by calculating the precision, recall, and f-measure of detection of phrase boundaries, with an exact match required for a 'hit'. The results of our experiments can be seen in Table II. Inspecting the left portion of Table II, we see that the total precision of our model in d ...