Automatic Extraction of Efficient Axiom Sets from Large Knowledge
... We have observed that ground facts are not uniformly distributed across predicates. Therefore while searching, we should focus on regions of the search space that (i) are rich in ground facts or (ii) involve facts that can be produced by external systems (e.g., machine learning, learning by reading, ...
... We have observed that ground facts are not uniformly distributed across predicates. Therefore while searching, we should focus on regions of the search space that (i) are rich in ground facts or (ii) involve facts that can be produced by external systems (e.g., machine learning, learning by reading, ...
Introduction to Bayesian Networks A Three Day Tutorial
... • Categorization of other algorithms – Exact – Simulation ...
... • Categorization of other algorithms – Exact – Simulation ...
Bayesian Spiking Neurons II: Learning
... more (or less) probable than average was xt when a spike was received from that synapse. Thus, the weights are positively or negatively incremented depending on whether the probability of xt tends to be larger or smaller than its running average at the moment of the synaptic input. Similarly, learni ...
... more (or less) probable than average was xt when a spike was received from that synapse. Thus, the weights are positively or negatively incremented depending on whether the probability of xt tends to be larger or smaller than its running average at the moment of the synaptic input. Similarly, learni ...
Real-Time Credit-Card Fraud Detection using Artificial Neural
... that it gets stuck in local minima and the error still remains the same. An evolutionary algorithm, Simulated Annealing and Genetic Algorithm, was given to solve this problem of local minima, among these algorithm simulated annealing is preferred because it takes less time in comparison with genetic ...
... that it gets stuck in local minima and the error still remains the same. An evolutionary algorithm, Simulated Annealing and Genetic Algorithm, was given to solve this problem of local minima, among these algorithm simulated annealing is preferred because it takes less time in comparison with genetic ...
Introduction I have been interested in artificial intelligence and
... understanding of mathematics is impossible to avoid however and the deeper you get into this topic the more mathematics you are going to have to learn). Finally, we’ll get to the fun bit. I’ll come up with a little project I will program and take you through one step at a time. It will be in this la ...
... understanding of mathematics is impossible to avoid however and the deeper you get into this topic the more mathematics you are going to have to learn). Finally, we’ll get to the fun bit. I’ll come up with a little project I will program and take you through one step at a time. It will be in this la ...
Probabilistic Label Trees for Efficient Large Scale Image
... label tree, for which wc 6= wc0 for any two sibling nodes c, c0 . Also, suppose that the support of p(x) is continuous in some part of the domain. Then as the dataset size increases, the structure and weights of the label tree learned by our algorithm converges to those of the true label tree Proof ...
... label tree, for which wc 6= wc0 for any two sibling nodes c, c0 . Also, suppose that the support of p(x) is continuous in some part of the domain. Then as the dataset size increases, the structure and weights of the label tree learned by our algorithm converges to those of the true label tree Proof ...
Changes in GABA Modulation During a Theta Cycle May Be
... inhibitory interneuron; W, the strength of recurrent excitatory connections from a2 to a2 and a3 to a3 ; W 0 , the strength of excitatory connections from a2 and a3 to the interneuron; −H, the strength of the inhibitory connections from this interneuron to a2 and a3 ; h, activation of the model inte ...
... inhibitory interneuron; W, the strength of recurrent excitatory connections from a2 to a2 and a3 to a3 ; W 0 , the strength of excitatory connections from a2 and a3 to the interneuron; −H, the strength of the inhibitory connections from this interneuron to a2 and a3 ; h, activation of the model inte ...
Issues in Temporal and Causal Inference
... [17, 7], NARS does not attempt to simulate the response time of the human brain. The system uses its (subjective) working cycle as the unit of time, not the (objective) time provided by the clock of the host computer, so as to achieve platform-independence in testing. For example, if a certain infer ...
... [17, 7], NARS does not attempt to simulate the response time of the human brain. The system uses its (subjective) working cycle as the unit of time, not the (objective) time provided by the clock of the host computer, so as to achieve platform-independence in testing. For example, if a certain infer ...
Spatial and temporal frequency selectivity of neurons in
... components (Campbell & Robson, 1968; Glezer et al., 1973; Maffei & Fiorentini, 1973). While at one stage this may have been seen as incompatible with feature-based representations (Hubel & Wiesel, 1962, 1968), physiological and psychophysical studies have since indicated that different Fourier chann ...
... components (Campbell & Robson, 1968; Glezer et al., 1973; Maffei & Fiorentini, 1973). While at one stage this may have been seen as incompatible with feature-based representations (Hubel & Wiesel, 1962, 1968), physiological and psychophysical studies have since indicated that different Fourier chann ...
Attractor concretion as a mechanism for the formation of context
... the AN to the wait state. The AN encodes the CS–US associations by making CS triggered transitions to the state that represents the value of the predicted US. The CS–US associations are learned by biasing the competition between the positive and the negative state. In particular, the competition bia ...
... the AN to the wait state. The AN encodes the CS–US associations by making CS triggered transitions to the state that represents the value of the predicted US. The CS–US associations are learned by biasing the competition between the positive and the negative state. In particular, the competition bia ...
Belief Revision in Multi-Agent Systems
... been informed about by other community members (either because an acquaintance has answered a query or because it has volunteered a piece of relevant information). In such cases a number of crucial decisions must be made about how the information provided by other agents should be treated - should i ...
... been informed about by other community members (either because an acquaintance has answered a query or because it has volunteered a piece of relevant information). In such cases a number of crucial decisions must be made about how the information provided by other agents should be treated - should i ...
Methods for reducing interference in the Complementary Learning
... can be viewed as a complete solution to the stability– plasticity problem. In this paper, we present solutions to both of these problems: † In section 2, we describe a new learning algorithm developed by Norman, Newman, Detre, and Polyn (2005) that leverages regular oscillations in feedback inhibiti ...
... can be viewed as a complete solution to the stability– plasticity problem. In this paper, we present solutions to both of these problems: † In section 2, we describe a new learning algorithm developed by Norman, Newman, Detre, and Polyn (2005) that leverages regular oscillations in feedback inhibiti ...
Rule Insertion and Rule Extraction from Evolving Fuzzy
... The traditional expert systems, based on a fixed set of rules, have significantly contributed to the development of AI and intelligent engineering systems in the past two years. Despite their success, more flexible tools for dynamic rule adaptation, rule extraction from data, and rule insertion in a ...
... The traditional expert systems, based on a fixed set of rules, have significantly contributed to the development of AI and intelligent engineering systems in the past two years. Despite their success, more flexible tools for dynamic rule adaptation, rule extraction from data, and rule insertion in a ...
Aalborg Universitet Nielsen, Jannie Sønderkær; Sørensen, John Dalsgaard
... using sampling. The accuracy of this conditional distribution highly affects the final result when inference is performed. An alternative to the discrete models is to use continuous model, where approximate inference methods are needed. Markov Chain Monte Carlo (MCMC) methods can be used to handle c ...
... using sampling. The accuracy of this conditional distribution highly affects the final result when inference is performed. An alternative to the discrete models is to use continuous model, where approximate inference methods are needed. Markov Chain Monte Carlo (MCMC) methods can be used to handle c ...
A Neuronal Model of Predictive Coding Accounting for the
... linking the stimuli within the past few hundred milliseconds. A memory of the recent past is needed to achieve such a goal. This memory has to keep the trace of two properties: the identity of the past inputs and the time elapsed since they occurred. We choose to model this function in the simplest ...
... linking the stimuli within the past few hundred milliseconds. A memory of the recent past is needed to achieve such a goal. This memory has to keep the trace of two properties: the identity of the past inputs and the time elapsed since they occurred. We choose to model this function in the simplest ...
A Project on Gesture Recognition with Neural Networks for
... additional overhead. To put all students on the same footing and allow them to concentrate on the material taught in class, we decided to go with several small projects that do not need large code bases. This technical report describes one particular project from the undergraduate and graduate versi ...
... additional overhead. To put all students on the same footing and allow them to concentrate on the material taught in class, we decided to go with several small projects that do not need large code bases. This technical report describes one particular project from the undergraduate and graduate versi ...
Auditory Nerve Stochasticity Impedes Category Learning: the Role
... Fig 1. Schematic representation of the full AN-CN-IC-A1 (A), the reduced AN-A1 (B) and the simple four-stage (C) models of the auditory brain. Blue circles represent excitatory (E) and red circles represent inhibitory (I) neurons. The connectivity within each stage of the models is demonstrated usin ...
... Fig 1. Schematic representation of the full AN-CN-IC-A1 (A), the reduced AN-A1 (B) and the simple four-stage (C) models of the auditory brain. Blue circles represent excitatory (E) and red circles represent inhibitory (I) neurons. The connectivity within each stage of the models is demonstrated usin ...
Streamlining the Detection of Accounting Fraud through Web
... them, are allowed to survive, weights’ final values tend to assume symmetrical values so that their summation is indeed dimension-free. Representations thus mimic ratios and can be interpreted similarly. After appropriate ratios are selected, analysts interpret their observed, company-specific devia ...
... them, are allowed to survive, weights’ final values tend to assume symmetrical values so that their summation is indeed dimension-free. Representations thus mimic ratios and can be interpreted similarly. After appropriate ratios are selected, analysts interpret their observed, company-specific devia ...
Guided Incremental Construction of Belief Networks
... a plausible subset. But if this subset is selected in advance, we cannot handle situations where implausible rules suddenly become plausible, for example, if the car contains unfamiliar belongings or small grey men with large eyes. Knowledge-based systems could be both more robust and generally appl ...
... a plausible subset. But if this subset is selected in advance, we cannot handle situations where implausible rules suddenly become plausible, for example, if the car contains unfamiliar belongings or small grey men with large eyes. Knowledge-based systems could be both more robust and generally appl ...
Learning place cells, grid cells and invariances: A unifying model
... These center-surround fields arrange in a hexagonal pattern – the closest packing of spheres in two dimensions; compare [41]. We find that the spacing of this pattern is determined by the inhibitory smoothness, whereas the orientation and the phase of the grid depend in decreasing order on the rando ...
... These center-surround fields arrange in a hexagonal pattern – the closest packing of spheres in two dimensions; compare [41]. We find that the spacing of this pattern is determined by the inhibitory smoothness, whereas the orientation and the phase of the grid depend in decreasing order on the rando ...
ICAISC 2004 Preliminary Program
... Smoking is prohibited at all conference events. Your conference badge is your admission to all events and sessions. The importance of the papers is not related to the form of the presentation. Overhead and computer projectors will be available on all oral sessions. Posters should be prepared with th ...
... Smoking is prohibited at all conference events. Your conference badge is your admission to all events and sessions. The importance of the papers is not related to the form of the presentation. Overhead and computer projectors will be available on all oral sessions. Posters should be prepared with th ...
disparity detection from stereo
... that they are not robust against wide variations in object surface properties and lighting conditions [10]. The network learning approaches in category (3) do not require a match between the left and right elements. Instead, the binocular stimuli with a specific disparity are matched with binocular ...
... that they are not robust against wide variations in object surface properties and lighting conditions [10]. The network learning approaches in category (3) do not require a match between the left and right elements. Instead, the binocular stimuli with a specific disparity are matched with binocular ...
Probabilistic State-Dependent Grammars for Plan
... generate hypotheses about which top-level plan or intermediate subplans the agent has selected, or which low-level actions it will perform in the future. The resulting candidates, as well as possible evaluations of their plausibilities, form the basis for decisions on potential interactions with the ...
... generate hypotheses about which top-level plan or intermediate subplans the agent has selected, or which low-level actions it will perform in the future. The resulting candidates, as well as possible evaluations of their plausibilities, form the basis for decisions on potential interactions with the ...
ARTICLE IN PRESS Neural Networks entorhinal cortex
... The mechanism of persistent spiking could code memories either in terms of the graded magnitude of firing rate (Egorov et al., 2002; Fransén et al., 2006), or in terms of the phase of spiking relative to the phase of a stable baseline frequency (Hasselmo, 2008a). Many models of cortex code memory in ...
... The mechanism of persistent spiking could code memories either in terms of the graded magnitude of firing rate (Egorov et al., 2002; Fransén et al., 2006), or in terms of the phase of spiking relative to the phase of a stable baseline frequency (Hasselmo, 2008a). Many models of cortex code memory in ...
Hierarchical temporal memory
Hierarchical temporal memory (HTM) is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.