A biologically constrained learning mechanism in networks of formal
... with nonorthogonal patterns (such as, for instance, random patterns with a finite number of neurons), the stability of the prototype patterns is no longer guaranteed; it is well-known that this problem is a strong limitation to the storage capacity of Hopfield networks. Similarly, the question which ...
... with nonorthogonal patterns (such as, for instance, random patterns with a finite number of neurons), the stability of the prototype patterns is no longer guaranteed; it is well-known that this problem is a strong limitation to the storage capacity of Hopfield networks. Similarly, the question which ...
Deep learning with COTS HPC systems
... many GPUs distributed over a large cluster). Unfortunately, obvious attempts to build large-scale systems based on this idea run across several major hurdles. First, attempting to build large clusters of GPUs is difficult due to communications bottlenecks. Consider, for instance, using widely-implem ...
... many GPUs distributed over a large cluster). Unfortunately, obvious attempts to build large-scale systems based on this idea run across several major hurdles. First, attempting to build large clusters of GPUs is difficult due to communications bottlenecks. Consider, for instance, using widely-implem ...
10.4. What follows from the fact that some neurons we consider
... A much worse situation will occur when input signals will be equally distributed in some area of input signal space, as it is shown in fig. 10.16. Then, neurons of the network will have tendency to “share” the function of recognizing these signals, so that each subset of signals will have its “guar ...
... A much worse situation will occur when input signals will be equally distributed in some area of input signal space, as it is shown in fig. 10.16. Then, neurons of the network will have tendency to “share” the function of recognizing these signals, so that each subset of signals will have its “guar ...
Sequence Learning: From Recognition and Prediction to
... many recurrent neural network models12 and even harder for reinforcement learning. Many heuristic methods might help facilitate learning of temporal dependencies somewhat,7,8 but they also break down in cases of long-range dependencies. Another issue is hierarchical structuring of sequences. Many re ...
... many recurrent neural network models12 and even harder for reinforcement learning. Many heuristic methods might help facilitate learning of temporal dependencies somewhat,7,8 but they also break down in cases of long-range dependencies. Another issue is hierarchical structuring of sequences. Many re ...
Design of Intelligent Machines Heidi 2005
... Learning should be restricted to unexpected situation or reward Anticipated response should have expected value Novelty detection should also apply to the value system Need mechanism to improve and compare the value Anticipated response block should learn the response that improves the value A RL op ...
... Learning should be restricted to unexpected situation or reward Anticipated response should have expected value Novelty detection should also apply to the value system Need mechanism to improve and compare the value Anticipated response block should learn the response that improves the value A RL op ...
Accelerometer and Video Based Human Activity Recognition
... [4] Mark A. Hall and Lloyd A. Smith. 1999. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. In Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, Amruth N. Kumar and Ingrid Russell (Eds.). AAAI Pr ...
... [4] Mark A. Hall and Lloyd A. Smith. 1999. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. In Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, Amruth N. Kumar and Ingrid Russell (Eds.). AAAI Pr ...
Forgetting
... • Individuals of all ages were claiming to suddenly remember events that had been “repressed” and forgotten for years. • Often these memories were of abuse. • Sometimes these recovered memories were corroborated with physical evidence and justice was served. • Other times they were discovered to be ...
... • Individuals of all ages were claiming to suddenly remember events that had been “repressed” and forgotten for years. • Often these memories were of abuse. • Sometimes these recovered memories were corroborated with physical evidence and justice was served. • Other times they were discovered to be ...
Steel Production and Its Uses
... As already stated inverting such a neural network would entail figuring out the inputs x' which correspond to a given output y'. This means the output space will now be mapped to the input space instead of being mapped from the input space. However the problem associated with this representation is ...
... As already stated inverting such a neural network would entail figuring out the inputs x' which correspond to a given output y'. This means the output space will now be mapped to the input space instead of being mapped from the input space. However the problem associated with this representation is ...
The Format of the IJOPCM, first submission
... during the development and production process must last through the distribution and consumption stages. Shelf life studies can provide important information to product developers enabling them to ensure that the consumer will get a high quality product for a significant period of time after its pr ...
... during the development and production process must last through the distribution and consumption stages. Shelf life studies can provide important information to product developers enabling them to ensure that the consumer will get a high quality product for a significant period of time after its pr ...
Evolution might select constructivism
... of the more complex complete task from this selected input. Both Elman (1993) and Goldowsky and Newport (1993) have demonstrated that neural network models can be made more effective in artificial language tasks by limiting their capacities in realistic ways. An illustration might help clarify the b ...
... of the more complex complete task from this selected input. Both Elman (1993) and Goldowsky and Newport (1993) have demonstrated that neural network models can be made more effective in artificial language tasks by limiting their capacities in realistic ways. An illustration might help clarify the b ...
temporal visual event recognition
... frame to the next. In [3], a local interconnected circuit model that learned to represent different timescales was presented. A key aspect to their ability to learn time was their short-term synaptic plasticity. This is the first time where the effect of internally generated expectation has been stu ...
... frame to the next. In [3], a local interconnected circuit model that learned to represent different timescales was presented. A key aspect to their ability to learn time was their short-term synaptic plasticity. This is the first time where the effect of internally generated expectation has been stu ...
Quo vadis, computational intelligence
... probabilities. This level of description is more detailed than the finite state automata, since each state is an objected represented in the feature space. Such models are a step from neural networks to networks representing lowlevel cognitive processes. They are tools to model processes taking plac ...
... probabilities. This level of description is more detailed than the finite state automata, since each state is an objected represented in the feature space. Such models are a step from neural networks to networks representing lowlevel cognitive processes. They are tools to model processes taking plac ...
Quo vadis, computational intelligence?
... plausible way [61], but rather complex networks are required. Adding one additional internal parameter (phase) is sufficient to solve this problem [32]. What is the complexity class of problems that may be solved this way? Can all problems of finding topological invariants be solved? What can be gai ...
... plausible way [61], but rather complex networks are required. Adding one additional internal parameter (phase) is sufficient to solve this problem [32]. What is the complexity class of problems that may be solved this way? Can all problems of finding topological invariants be solved? What can be gai ...
A NEAT Approach to Neural Network Structure
... exact frequency of each operation can be adjusted by most NEAT implementations. The following diagram shows a typical NEAT genome. You can see from the above that input 2 was disregarded. You can also see that the layers are not clearly defined. There are recurrent connections, and even connections ...
... exact frequency of each operation can be adjusted by most NEAT implementations. The following diagram shows a typical NEAT genome. You can see from the above that input 2 was disregarded. You can also see that the layers are not clearly defined. There are recurrent connections, and even connections ...
CS 561a: Introduction to Artificial Intelligence
... Associative memory with Hopfield nets • Setup a Hopfield net such that local minima correspond to the stored patterns. • Issues: - because of weight symmetry, anti-patterns (binary reverse) are stored as well as the original patterns (also spurious local minima are created when many patterns are st ...
... Associative memory with Hopfield nets • Setup a Hopfield net such that local minima correspond to the stored patterns. • Issues: - because of weight symmetry, anti-patterns (binary reverse) are stored as well as the original patterns (also spurious local minima are created when many patterns are st ...
Machine learning and the brain - Intelligent Autonomous Systems
... most of the time and is often accompanied by various simulations. This lasts for weeks or even months, a timespan which is hardly thinkable in computer-science. The experiment itself is rather short, whereas the evaluation again depends on the gathered information. Those experiments can be distingui ...
... most of the time and is often accompanied by various simulations. This lasts for weeks or even months, a timespan which is hardly thinkable in computer-science. The experiment itself is rather short, whereas the evaluation again depends on the gathered information. Those experiments can be distingui ...
PerceptronNNIntro200..
... • Let F be a set of unit length vectors. If there is a (unit) vector V* and a value e>0 such that V*X > e for all X in F then the perceptron program goes to FIX only a finite number of times (regardless of the order of choice of vectors X). ...
... • Let F be a set of unit length vectors. If there is a (unit) vector V* and a value e>0 such that V*X > e for all X in F then the perceptron program goes to FIX only a finite number of times (regardless of the order of choice of vectors X). ...
NSOM: A Real-Time Network-Based Intrusion Detection System
... classification results as being redundant background. NSOM could be changed to allow including the entire IP address if this behavior is desired. Another important feature that we keep in the process of representing a packet is the protocol type. Protocol type can include and TCP/IP or UDP. All the ...
... classification results as being redundant background. NSOM could be changed to allow including the entire IP address if this behavior is desired. Another important feature that we keep in the process of representing a packet is the protocol type. Protocol type can include and TCP/IP or UDP. All the ...
cooperative artificial immune system and recurrent neural
... are difficult to be generated. In the experiments, GM represents the matching times between all candidate detectors and the self individuals during the generation of detectors. The parallel structure of BAMs has been employed as a correction unit. This error correction process is initiated for a 128 ...
... are difficult to be generated. In the experiments, GM represents the matching times between all candidate detectors and the self individuals during the generation of detectors. The parallel structure of BAMs has been employed as a correction unit. This error correction process is initiated for a 128 ...
Computational Intelligence
... Silicon-based computational intelligence systems usually comprise hybrids of paradigms such as artificial neural networks, fuzzy systems, and evolutionary algorithms, augmented with knowledge elements, and are often designed to mimic one or more aspects of carbon-based biological intelligence. The c ...
... Silicon-based computational intelligence systems usually comprise hybrids of paradigms such as artificial neural networks, fuzzy systems, and evolutionary algorithms, augmented with knowledge elements, and are often designed to mimic one or more aspects of carbon-based biological intelligence. The c ...
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.