Intelligence - Ohio University
... Embodied Intelligence (EI) is a mechanism that learns how to survive in a hostile environment – Mechanism: biological, mechanical or virtual agent with embodied sensors and actuators – EI acts on environment and perceives its actions – Environment hostility is persistent and stimulates EI to act – ...
... Embodied Intelligence (EI) is a mechanism that learns how to survive in a hostile environment – Mechanism: biological, mechanical or virtual agent with embodied sensors and actuators – EI acts on environment and perceives its actions – Environment hostility is persistent and stimulates EI to act – ...
Artificial Intelligence
... directory) to navigate to the directory where you have downloaded and unzipped the files, and then simply type (case sensitive) the name of the file without the “.m” extension. – E.g. to run the digit_recognition.m you type digit_recognition on Matlab’s command window. ...
... directory) to navigate to the directory where you have downloaded and unzipped the files, and then simply type (case sensitive) the name of the file without the “.m” extension. – E.g. to run the digit_recognition.m you type digit_recognition on Matlab’s command window. ...
Extended breadth-first search algorithm in practice
... • B = {b1 , b2 , . . . , bm } is a set of “backward” functions, bi ∈ (2S )S . The “forward” and “backward” functions represent the direct connections between states. Using the model described above, we are able to represent heuristic with the help of initially known states instead of creating approx ...
... • B = {b1 , b2 , . . . , bm } is a set of “backward” functions, bi ∈ (2S )S . The “forward” and “backward” functions represent the direct connections between states. Using the model described above, we are able to represent heuristic with the help of initially known states instead of creating approx ...
Supervised and Unsupervised Neural Networks
... Biological neural networks are much more complicated in their elementary structures than the mathematical models we use for ANNs. It is an inherently multiprocessor-friendly architecture and without much modification, it goes beyond one or even two processors of the von Neumann architecture. It has ...
... Biological neural networks are much more complicated in their elementary structures than the mathematical models we use for ANNs. It is an inherently multiprocessor-friendly architecture and without much modification, it goes beyond one or even two processors of the von Neumann architecture. It has ...
... Figure 10: Simplified neuron model [4] Here the input vector p is represented by the solid dark vertical bar at the left. The dimensions of p are shown below the symbol p in the figure as Rx1. (Note that a capital letter, such as R in the previous sentence, is used when referring to the size of a ve ...
Evolving Fuzzy Neural Networks - Algorithms, Applications
... 234 condition nodes (three fuzzy membership functions per input), 10 rule nodes, two action nodes, and one output. This architecture is identical to that used for the speech recognition system described in [6]. Nine networks were created and trained for 1000 epochs for each phoneme. A bootstrap meth ...
... 234 condition nodes (three fuzzy membership functions per input), 10 rule nodes, two action nodes, and one output. This architecture is identical to that used for the speech recognition system described in [6]. Nine networks were created and trained for 1000 epochs for each phoneme. A bootstrap meth ...
Application of AI- and ML-Techniques to FT
... – Router Control (4 bits): Type of message, including NORMAL and BACKTRACK – Destination Node ID (10 bits): Supports network of size up to 1024 nodes – Pending Nodes (20 bytes): Stack of node IDs that may receive packet but have not yet – Traversed Nodes (20 bytes): Stack of nodes traversed, with mo ...
... – Router Control (4 bits): Type of message, including NORMAL and BACKTRACK – Destination Node ID (10 bits): Supports network of size up to 1024 nodes – Pending Nodes (20 bytes): Stack of node IDs that may receive packet but have not yet – Traversed Nodes (20 bytes): Stack of nodes traversed, with mo ...
BJ4102451460
... independent of how it is perceived such as visual, auditory or any other sensory inputs. In this work it is assumed that each episode is represented in sentential form comprising of episodic entities. Each episode comprises of entities, temporal and spatial information such as who, what, when, where ...
... independent of how it is perceived such as visual, auditory or any other sensory inputs. In this work it is assumed that each episode is represented in sentential form comprising of episodic entities. Each episode comprises of entities, temporal and spatial information such as who, what, when, where ...
1 CHAPTER 2 LITERATURE REVIEW 2.1 Music Fundamentals 2.1
... neural nets trained by backpropagation, because the simple relationship between the value of the function at a point and the value of the derivative at that point reduces the computational burden during training. The logistic function, a sigmoid function with range from 0 to 1, is often used as the ...
... neural nets trained by backpropagation, because the simple relationship between the value of the function at a point and the value of the derivative at that point reduces the computational burden during training. The logistic function, a sigmoid function with range from 0 to 1, is often used as the ...
Learning receptive fields using predictive feedback
... and the next neuron is chosen by again determining which of the remaining V1 basis vectors best predicts this residual input. In a neural network, the subtractive process is carried out using feedback connections, so that at each iteration of the algorithm the residual input is described by the acti ...
... and the next neuron is chosen by again determining which of the remaining V1 basis vectors best predicts this residual input. In a neural network, the subtractive process is carried out using feedback connections, so that at each iteration of the algorithm the residual input is described by the acti ...
T R ECHNICAL ESEARCH
... • The nodes represent variables of interest (propositions), which maybe be discrete, assuming values from finite or countable states, or may be continuous. • The set of directed links or arrows represent the causal influence among the variables and the parents of a node are all those nodes with arro ...
... • The nodes represent variables of interest (propositions), which maybe be discrete, assuming values from finite or countable states, or may be continuous. • The set of directed links or arrows represent the causal influence among the variables and the parents of a node are all those nodes with arro ...
Pattern Recognition by Labeled Graph Matching
... natural scenes which are to be processed as input. If neural systems are to absorb information from one scene and apply it to another, they have to be capable of generalization. Important types of generalization can be based on the decomposition of scenes into standard objects and on object recognit ...
... natural scenes which are to be processed as input. If neural systems are to absorb information from one scene and apply it to another, they have to be capable of generalization. Important types of generalization can be based on the decomposition of scenes into standard objects and on object recognit ...
the original powerpoint file
... Fine-tuning with a contrastive divergence version of the wake-sleep algorithm • Replace the top layer of the causal network by an RBM – This eliminates explaining away at the top-level. – It is nice to have an associative memory at the top. • Replace the sleep phase by a top-down pass starting with ...
... Fine-tuning with a contrastive divergence version of the wake-sleep algorithm • Replace the top layer of the causal network by an RBM – This eliminates explaining away at the top-level. – It is nice to have an associative memory at the top. • Replace the sleep phase by a top-down pass starting with ...
Metody Inteligencji Obliczeniowej
... Neural information processing in perception and cognition: information compression, or algorithmic complexity. In computing: minimum length (message, description) encoding. Wolff (2006): all cognition and computation as compression! Analysis and production of natural language, fuzzy pattern recognit ...
... Neural information processing in perception and cognition: information compression, or algorithmic complexity. In computing: minimum length (message, description) encoding. Wolff (2006): all cognition and computation as compression! Analysis and production of natural language, fuzzy pattern recognit ...
Learning Text Similarity with Siamese Recurrent
... taxonomy. The job titles were manually and semiautomatically collected from résumés and vacancy postings. Each was manually assigned a group, such that the job titles in a group are close together in meaning. In some cases this closeness is an expression of a (near-)synonymy relation between the j ...
... taxonomy. The job titles were manually and semiautomatically collected from résumés and vacancy postings. Each was manually assigned a group, such that the job titles in a group are close together in meaning. In some cases this closeness is an expression of a (near-)synonymy relation between the j ...
A Computer Simulation of Olfactory Cortex with Functional
... Synaptic Properties and Modification Rules. In the model, each synaptic connection has an associated weight which determines the peak amplitude of the conductance change induced in the postsynaptic cell following presynaptic activity [2.0]. To study learning in the model, synaptic weights associated ...
... Synaptic Properties and Modification Rules. In the model, each synaptic connection has an associated weight which determines the peak amplitude of the conductance change induced in the postsynaptic cell following presynaptic activity [2.0]. To study learning in the model, synaptic weights associated ...
Training
... The hidden neurons define the state of the network. The output of the hidden layer is fed back to the input layer via a bank of unit delays. The input layer consists of a concatenation of feedback nodes and source nodes. The network is connected to the external environment via the source node. The n ...
... The hidden neurons define the state of the network. The output of the hidden layer is fed back to the input layer via a bank of unit delays. The input layer consists of a concatenation of feedback nodes and source nodes. The network is connected to the external environment via the source node. The n ...
An introduction to graphical models
... many advantages – in particular, specialized techniques that have been developed in one field can be transferred between research communities and exploited more widely. Moreover, the graphical model formalism provides a natural framework for the design of new systems. In this paper, we will flesh ou ...
... many advantages – in particular, specialized techniques that have been developed in one field can be transferred between research communities and exploited more widely. Moreover, the graphical model formalism provides a natural framework for the design of new systems. In this paper, we will flesh ou ...
HTM Neuron paper 12-1
... enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hun ...
... enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hun ...
Image Pattern Recognition
... • Linear and Non-Linear Classifiers – E.g. neural network and perceptron ...
... • Linear and Non-Linear Classifiers – E.g. neural network and perceptron ...
Pathfinding in Computer Games 1 Introduction
... E – Edges: A set of connections between the vertices, which can be either directed or not ...
... E – Edges: A set of connections between the vertices, which can be either directed or not ...
poster - Xiannian Fan
... For a 8-variable problem, partition all the variables by Simple Grouping (SG) into two groups: G1={X1, X2, X3, X4}, G2={X5, X6, X7, X8}. We created the pattern databases with a backward breadth-first search in the order graph for each group. ...
... For a 8-variable problem, partition all the variables by Simple Grouping (SG) into two groups: G1={X1, X2, X3, X4}, G2={X5, X6, X7, X8}. We created the pattern databases with a backward breadth-first search in the order graph for each group. ...
Neural Networks
... For bipolar signals the outputs for the two classes are -1 and +1. For unipolar signals it is 0 and 1. Depending on the number of inputs the decision boundary can be a line, plane or a hyperplane. Eg. For two inputs its a line and for three inputs its a plane. If all of the training input vectors fo ...
... For bipolar signals the outputs for the two classes are -1 and +1. For unipolar signals it is 0 and 1. Depending on the number of inputs the decision boundary can be a line, plane or a hyperplane. Eg. For two inputs its a line and for three inputs its a plane. If all of the training input vectors fo ...
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.