Learning Bayesian networks: The combination of knowledge and
... knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as weil as the assumption of likelihood equivalence, which says that data should not help to discrimina ...
... knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as weil as the assumption of likelihood equivalence, which says that data should not help to discrimina ...
A Bayesian network primer
... distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure. A local probabilistic model is attached to each node to quantify the stochasti ...
... distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure. A local probabilistic model is attached to each node to quantify the stochasti ...
Contraction Properties of VLSI Cooperative Competitive Neural
... multi–stability [2, 5]. CCN networks can be modeled using linear threshold units, as well as recurrent networks of spiking neurons. The latter can be efficiently implemented in silicon using Integrate–and–Fire (I&F) neurons and dynamic synapses [7]. In this work we use a prototype VLSI CCN device, c ...
... multi–stability [2, 5]. CCN networks can be modeled using linear threshold units, as well as recurrent networks of spiking neurons. The latter can be efficiently implemented in silicon using Integrate–and–Fire (I&F) neurons and dynamic synapses [7]. In this work we use a prototype VLSI CCN device, c ...
PDF file
... development of disparity tuning in toy objects data using an artificial neural network based on back-propagation and reinforcement learning. They reported 90% correct recognition rate for 11 classes of disparity. In Solgi & Weng 2008 [13], a multilayer in-place learning network was used to detect bi ...
... development of disparity tuning in toy objects data using an artificial neural network based on back-propagation and reinforcement learning. They reported 90% correct recognition rate for 11 classes of disparity. In Solgi & Weng 2008 [13], a multilayer in-place learning network was used to detect bi ...
AAAI Proceedings Template
... processor composed of uncomplicated processing units, which has an inborn tendency to retain knowledge gained through experience and to be ready for use [Haykin, 2008]. ANNs, also known simply as neural networks, are constructed on the model of biological neural networks and are similar to it, but d ...
... processor composed of uncomplicated processing units, which has an inborn tendency to retain knowledge gained through experience and to be ready for use [Haykin, 2008]. ANNs, also known simply as neural networks, are constructed on the model of biological neural networks and are similar to it, but d ...
Belief Updating by Enumerating High-Probability
... stochastically sampling through instantiations to the network variables. In [10], the idea was to use the con ditioning method, but to condition only on a small, high probability, subset of the (exponential size) set of possible assignments to the cutset variable. Recently, approximation algorithms ...
... stochastically sampling through instantiations to the network variables. In [10], the idea was to use the con ditioning method, but to condition only on a small, high probability, subset of the (exponential size) set of possible assignments to the cutset variable. Recently, approximation algorithms ...
ANN Models Optimized using Swarm Intelligence Algorithms
... measurement. Measurement can be used to better understand the attributes of the models that are created and used to assess the quality of engineered products or system built. Measure of internal product attributes provides a real-time indication of the efficacy of the requirements, design, code and ...
... measurement. Measurement can be used to better understand the attributes of the models that are created and used to assess the quality of engineered products or system built. Measure of internal product attributes provides a real-time indication of the efficacy of the requirements, design, code and ...
Down - 서울대 : Biointelligence lab
... These rats were not able to recover normal tonotopic representation in A1 even though stimulated with sounds of difficult frequencies However when the same sound patterns were used to solve to get a food reward, rats were able to recover a normal tonotopic maps (Fig 7.6B) ...
... These rats were not able to recover normal tonotopic representation in A1 even though stimulated with sounds of difficult frequencies However when the same sound patterns were used to solve to get a food reward, rats were able to recover a normal tonotopic maps (Fig 7.6B) ...
Unifying Logical and Statistical AI - Washington
... 1. ML,C contains one binary node for each possible grounding of each predicate appearing in L. The value of the node is 1 if the ground predicate is true, and 0 otherwise. 2. ML,C contains one feature for each possible grounding of each formula Fi in L. The value of this feature is 1 if the ground f ...
... 1. ML,C contains one binary node for each possible grounding of each predicate appearing in L. The value of the node is 1 if the ground predicate is true, and 0 otherwise. 2. ML,C contains one feature for each possible grounding of each formula Fi in L. The value of this feature is 1 if the ground f ...
On the relevance of time in neural computation and learning
... For some speci=ed subset Vin ⊆ V of input neurons one assumes that the =ring times (“spike trains”) Fu for neurons u ∈ Vin are not de=ned by the preceding convention, but are given from the outside as input to the network. The =ring times Fv for all other neurons v ∈ V are determined by the previous ...
... For some speci=ed subset Vin ⊆ V of input neurons one assumes that the =ring times (“spike trains”) Fu for neurons u ∈ Vin are not de=ned by the preceding convention, but are given from the outside as input to the network. The =ring times Fv for all other neurons v ∈ V are determined by the previous ...
LINKS BETWEEN LTP AND LEARNING AND MEMORY
... learn the general task requirement as well as the specific location of the hidden platform Non-spatial pretraining can separate the two kinds of learning Rats first made familiar with the general task requirements and subsequently trained after receiving NMDAR antagonists could learn the spatial loc ...
... learn the general task requirement as well as the specific location of the hidden platform Non-spatial pretraining can separate the two kinds of learning Rats first made familiar with the general task requirements and subsequently trained after receiving NMDAR antagonists could learn the spatial loc ...
Artificial Neural Network in Drug Delivery and Pharmaceutical
... or continue evolving and learning though interactions with The learning through weight adjustment can be supervised or datasets. If the data is noisy, overtraining of the ANN may unsupervised. The network is repeatedly presented with an be of a concern as it may lose the ability to generalize and in ...
... or continue evolving and learning though interactions with The learning through weight adjustment can be supervised or datasets. If the data is noisy, overtraining of the ANN may unsupervised. The network is repeatedly presented with an be of a concern as it may lose the ability to generalize and in ...
Cooperation and biased competition model can explain attentional
... over the other (cf. ®g. 1 in Everling et al., 2002). Next, during the focused attention task, these neurons again discriminated between target and nontarget, but only when the stimulus changed in the attended location. The stimulus in the nonattended location had no in¯uence on the neuronal response ...
... over the other (cf. ®g. 1 in Everling et al., 2002). Next, during the focused attention task, these neurons again discriminated between target and nontarget, but only when the stimulus changed in the attended location. The stimulus in the nonattended location had no in¯uence on the neuronal response ...
Connectionism and Information Processing Abstractions
... To better understand the difference between the symbolic and nonsymbolic approaches, let us consider the problem of multiplying two positive integers. We are all familiar with algorithms to perform this task. We also know how the traditional slide rule can be used to do this multiplication. The mult ...
... To better understand the difference between the symbolic and nonsymbolic approaches, let us consider the problem of multiplying two positive integers. We are all familiar with algorithms to perform this task. We also know how the traditional slide rule can be used to do this multiplication. The mult ...
Signal Propagation and Logic Gating in Networks of Integrate
... rather than reporting the values of ⌬gex and ⌬ginh, which are the synaptic strengths, we report the resulting EPSP and IPSP sizes. These are obtained within the active network from spike-triggered average membrane potentials of postsynaptic neurons after spikes evoked within individual network neuro ...
... rather than reporting the values of ⌬gex and ⌬ginh, which are the synaptic strengths, we report the resulting EPSP and IPSP sizes. These are obtained within the active network from spike-triggered average membrane potentials of postsynaptic neurons after spikes evoked within individual network neuro ...
On-line Error Analysis Using AI techniques A first sight
... “Subsystem Supervisor” using AI techniques. Given the high number of data sources and the huge amount of data to be processed, the problem's solution is not trivial, since even a small percentage of wrong alerts given to an operator can lead the whole system's administration to confusion. Another po ...
... “Subsystem Supervisor” using AI techniques. Given the high number of data sources and the huge amount of data to be processed, the problem's solution is not trivial, since even a small percentage of wrong alerts given to an operator can lead the whole system's administration to confusion. Another po ...
- Wiley Online Library
... to focus more on their weaknesses early on, such that their performance improved more in HVT than in FPT. Contrary to previous claims17 that VPT could lead to transfer of skills to untrained tasks (often called ‘far transfer’, as opposed to ‘near transfer’, in which the trained and untrained tasks a ...
... to focus more on their weaknesses early on, such that their performance improved more in HVT than in FPT. Contrary to previous claims17 that VPT could lead to transfer of skills to untrained tasks (often called ‘far transfer’, as opposed to ‘near transfer’, in which the trained and untrained tasks a ...
Towards Modeling False Memory with Computational Knowledge
... as a viable explanation for false memory in the DRM task. The semantic network used in this experiment was created manually from the words in the “needle” and “doctor” lists. For each list, the fifteen stimuli words are all connected to the lure, with additional connections created based on whether ...
... as a viable explanation for false memory in the DRM task. The semantic network used in this experiment was created manually from the words in the “needle” and “doctor” lists. For each list, the fifteen stimuli words are all connected to the lure, with additional connections created based on whether ...
Fuzzy Systems and Neuro-Computing in Credit Approval
... deal with inexact information. Traditional computational techniques, such as statistical models and neural networks, require precision—on/off, yes/no, right/wrong. However, human beings do not experience the world this way; many of our activities and decisions are inexact. Fuzzy logic achieves a tra ...
... deal with inexact information. Traditional computational techniques, such as statistical models and neural networks, require precision—on/off, yes/no, right/wrong. However, human beings do not experience the world this way; many of our activities and decisions are inexact. Fuzzy logic achieves a tra ...
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.