Methods of Artificial Intelligence in Blind People Education
... practical systems. But neurocomputing architectures are successfully applicable to many reallife problems. The single or multilayer fully connected feedforward or feedback networks can be used for character recognition, c.f. [8]. Experimental tests were targeted on classification of quarter, eight a ...
... practical systems. But neurocomputing architectures are successfully applicable to many reallife problems. The single or multilayer fully connected feedforward or feedback networks can be used for character recognition, c.f. [8]. Experimental tests were targeted on classification of quarter, eight a ...
Fading memory and kernel properties of generic cortical microcircuit
... attractor neural networks on one hand (which both use widely varying task-dependent computation times until they provide an output) and many cortical computations on the other hand, is that the latter often have to provide an output within a specific and rather short deadline. Hence cortical microcir ...
... attractor neural networks on one hand (which both use widely varying task-dependent computation times until they provide an output) and many cortical computations on the other hand, is that the latter often have to provide an output within a specific and rather short deadline. Hence cortical microcir ...
Guided Incremental Construction of Belief Networks
... and a set of conditional distributions that give a local distribution for variables based on their parents in the graph. Belief networks are described formally in Russell and Norvig [16]. Belief networks that describe several similar objects often have repetitive structure. For example, in the milit ...
... and a set of conditional distributions that give a local distribution for variables based on their parents in the graph. Belief networks are described formally in Russell and Norvig [16]. Belief networks that describe several similar objects often have repetitive structure. For example, in the milit ...
November 2000 Volume 3 Number Supp pp 1205
... top-down simulations of eyelid conditioning have been stated clearly32. First, devise real-time computational models that describe as much of the known behavioral and physiological evidence as possible. Second, devise an implementation scheme that aligns features of the model with the neural circuit ...
... top-down simulations of eyelid conditioning have been stated clearly32. First, devise real-time computational models that describe as much of the known behavioral and physiological evidence as possible. Second, devise an implementation scheme that aligns features of the model with the neural circuit ...
Neural Networks
... Want to learn not only by reading, but also by coding? Use SNIPE! SNIPE 1 is a well-documented JAVA library that implements a framework for neural networks in a speedy, feature-rich and usable way. It is available at no cost for non-commercial purposes. It was originally designed for high performanc ...
... Want to learn not only by reading, but also by coding? Use SNIPE! SNIPE 1 is a well-documented JAVA library that implements a framework for neural networks in a speedy, feature-rich and usable way. It is available at no cost for non-commercial purposes. It was originally designed for high performanc ...
Document
... learning representations. An observation (e.g., an image) can be represented in many ways (e.g., a vector of pixels), but some representations make it easier to learn tasks of interest (e.g., is this the image of a human face?) from examples, and research in this area attempts to define what makes b ...
... learning representations. An observation (e.g., an image) can be represented in many ways (e.g., a vector of pixels), but some representations make it easier to learn tasks of interest (e.g., is this the image of a human face?) from examples, and research in this area attempts to define what makes b ...
Using Partial Global Plans to Coordinate Distributed Problem
... about the plan's objective, the order of major plan steps, how long each is expected to take, and detailed actions (KSs) that have been taken or will be taken. n o d e - p l a n : The representation of a plan that nodes communicate about. Contains information about the plan's objective, the order of ...
... about the plan's objective, the order of major plan steps, how long each is expected to take, and detailed actions (KSs) that have been taken or will be taken. n o d e - p l a n : The representation of a plan that nodes communicate about. Contains information about the plan's objective, the order of ...
Neural Networks
... commercial off-the-shelf products – some let you train networks and use them with no more knowledge than needed for the real estate appraisal example Slide 29 ...
... commercial off-the-shelf products – some let you train networks and use them with no more knowledge than needed for the real estate appraisal example Slide 29 ...
Powerpoint slides - Computer Science
... Cunningham, P., Nowlan, N., Delany, S.J. & Haahr, M.: A Case-Based Approach to Spam Filtering that can Track Concept Drift, Long-Lived CBR Systems Workshop, 5th ICCBR , pp.115-123, 2003 Delany, S.J., Cunningham, P., Tsymbal, A. & Coyle, L.: A Case-Based Technique for Tracking Concept Drift in Spam F ...
... Cunningham, P., Nowlan, N., Delany, S.J. & Haahr, M.: A Case-Based Approach to Spam Filtering that can Track Concept Drift, Long-Lived CBR Systems Workshop, 5th ICCBR , pp.115-123, 2003 Delany, S.J., Cunningham, P., Tsymbal, A. & Coyle, L.: A Case-Based Technique for Tracking Concept Drift in Spam F ...
Bayesian Computation in Recurrent Neural Circuits
... and sensory evidence. Bayes’ rule prescribes how prior probabilities and stimulus likelihoods should be combined, allowing the responses of subjects during psychophysical tasks to be interpreted in terms of the resulting posterior distributions. Neural Computation 16, 1–38 (2004) ...
... and sensory evidence. Bayes’ rule prescribes how prior probabilities and stimulus likelihoods should be combined, allowing the responses of subjects during psychophysical tasks to be interpreted in terms of the resulting posterior distributions. Neural Computation 16, 1–38 (2004) ...
Large-scale cognitive model design using the Nengo neural simulator
... encoding the stim input. The linear decoding is automatically computed for you, and used to generate the Value plots. This principle naturally extends to multidimensional vector representations (see the 07-multipledimensions.py example). The default parameters of LIF neurons in the ensemble are rand ...
... encoding the stim input. The linear decoding is automatically computed for you, and used to generate the Value plots. This principle naturally extends to multidimensional vector representations (see the 07-multipledimensions.py example). The default parameters of LIF neurons in the ensemble are rand ...
3. Model based Diagnostic Systems
... of types of hardware components and a large number of them. The construction of network models involves the identification of all the necessary knowledge for the diagnostic task. Besides the identification of the necessary models, they have to be organized in a certain way that makes the existents r ...
... of types of hardware components and a large number of them. The construction of network models involves the identification of all the necessary knowledge for the diagnostic task. Besides the identification of the necessary models, they have to be organized in a certain way that makes the existents r ...
introduction to artificial intelligence and expert systems
... ◦ interpreter analyzes and processes the rules ◦ scheduler determines which rule to look at next ◦ the search portion of a rule-based system ◾ takes advantage of heuristic information ◾ otherwise, the time to solve a problem could become prohibitively long ◾ this problem is called the combinatorial ...
... ◦ interpreter analyzes and processes the rules ◦ scheduler determines which rule to look at next ◦ the search portion of a rule-based system ◾ takes advantage of heuristic information ◾ otherwise, the time to solve a problem could become prohibitively long ◾ this problem is called the combinatorial ...
Mental Processes -- How the Mind Arises from the Brain Roger Ellman
... - recognition of the letter E, whether capital or lower case, hand written or mechanically produced, large or small, alone or among other symbols, even though the particular E being recognized may be different from any ever before seen; - recognition of all beings that are human as human beings; - r ...
... - recognition of the letter E, whether capital or lower case, hand written or mechanically produced, large or small, alone or among other symbols, even though the particular E being recognized may be different from any ever before seen; - recognition of all beings that are human as human beings; - r ...
pdf 2.5M
... These particular models adopt a continuous-time setting and feature nonlinear network properties. The investigation of the neurons’ dynamics is assisted by general knowledge of properties of nonlinear oscillators, as well as of generic networks. In the references above, rather abstract models such a ...
... These particular models adopt a continuous-time setting and feature nonlinear network properties. The investigation of the neurons’ dynamics is assisted by general knowledge of properties of nonlinear oscillators, as well as of generic networks. In the references above, rather abstract models such a ...
The Neural Basis of the Object Concept in Ambiguous and
... (Fig. 1b). This network implements binding within a single feature dimension and will be called a feature module. A mathematical analysis of a single oscillator as well as of the network has been carried out by Maye (2002). The current work extends this model to multiple features. Here, the network ...
... (Fig. 1b). This network implements binding within a single feature dimension and will be called a feature module. A mathematical analysis of a single oscillator as well as of the network has been carried out by Maye (2002). The current work extends this model to multiple features. Here, the network ...
Mastering the game of Go with deep neural networks and tree search
... high-performance MCTS algorithms. In addition, we included the open source program GnuGo, a Go program using state-of-the-art search methods that preceded MCTS. All programs were allowed 5 seconds of computation time per move. The results of the tournament (see Figure 4,a) suggest that single machin ...
... high-performance MCTS algorithms. In addition, we included the open source program GnuGo, a Go program using state-of-the-art search methods that preceded MCTS. All programs were allowed 5 seconds of computation time per move. The results of the tournament (see Figure 4,a) suggest that single machin ...
PDF - Tuan Anh Le
... models as programs that include sample and observe statements (Gordon et al., 2014). Both sample and observe are functions that specify random variables in this generative model using probability distribution objects as an argument, while observe, in addition, specifies the conditioning of this rand ...
... models as programs that include sample and observe statements (Gordon et al., 2014). Both sample and observe are functions that specify random variables in this generative model using probability distribution objects as an argument, while observe, in addition, specifies the conditioning of this rand ...
Understanding and Improving Local Exploration for GBFS
... but smaller local searches may be a good tradeoff. To investigate this with minimal changes to the algorithm, the proposed new scheme GBFS-LS-X ×Y , where X ×Y = 1000, runs X local searches with Y expansions each from X random nodes in the best (minimum h) bucket in the open list. If there are fewer ...
... but smaller local searches may be a good tradeoff. To investigate this with minimal changes to the algorithm, the proposed new scheme GBFS-LS-X ×Y , where X ×Y = 1000, runs X local searches with Y expansions each from X random nodes in the best (minimum h) bucket in the open list. If there are fewer ...
HTM Neuron paper 12-1
... Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple pa ...
... Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple pa ...
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.