File
... 2. Define informational equivalence and computational equivalence. A transformation from on representation to another causes no loss of information; they can be constructed from each other. The same information and the same inferences are achieved with the same amount of effort. 3. Define knowledge ...
... 2. Define informational equivalence and computational equivalence. A transformation from on representation to another causes no loss of information; they can be constructed from each other. The same information and the same inferences are achieved with the same amount of effort. 3. Define knowledge ...
Artificial Neural Networks
... § In 1949, Donald Hebb proposed one of the key ideas in biological learning, commonly known as Hebb’s Law. § Hebb’s Law states that if neuron i is near enough to excite neuron j and repeatedly participates in its activation, the synaptic connection between these two neurons is strengthened and neuro ...
... § In 1949, Donald Hebb proposed one of the key ideas in biological learning, commonly known as Hebb’s Law. § Hebb’s Law states that if neuron i is near enough to excite neuron j and repeatedly participates in its activation, the synaptic connection between these two neurons is strengthened and neuro ...
National Institute of Education
... the user is oblivious to, but totally immersed within. Winn and Jackson also suggest that a VE learning interface in certain circumstances may be better than normal reality, where concepts and ideas can be simulated within an alternative sensual framework. For example, they suggest a VE in which a u ...
... the user is oblivious to, but totally immersed within. Winn and Jackson also suggest that a VE learning interface in certain circumstances may be better than normal reality, where concepts and ideas can be simulated within an alternative sensual framework. For example, they suggest a VE in which a u ...
Full Text PDF - Science and Education Publishing
... Abstract This article addresses an interesting comparative analytical study. The presented study considers two concepts of diverse algorithmic biological behavioral learning approach. Those concepts for computational intelligence are tightly related to neural and non-neural Systems. Respectively, th ...
... Abstract This article addresses an interesting comparative analytical study. The presented study considers two concepts of diverse algorithmic biological behavioral learning approach. Those concepts for computational intelligence are tightly related to neural and non-neural Systems. Respectively, th ...
Peering into the Future Through the Looking Glass of Artificial
... • Neural networks are a type of machine learning, and deep learning refers to one particular kind • Neural networks -- also known as "artificial" neural networks -- are one type of machine learning that's loosely based on how neurons work in the brain, though "the actual similarity is very minor” ...
... • Neural networks are a type of machine learning, and deep learning refers to one particular kind • Neural networks -- also known as "artificial" neural networks -- are one type of machine learning that's loosely based on how neurons work in the brain, though "the actual similarity is very minor” ...
poster - Xiannian Fan
... Its main idea is to relax the acyclicity constraint between groups of variables; acyclicity is enforced among the variables within 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 patte ...
... Its main idea is to relax the acyclicity constraint between groups of variables; acyclicity is enforced among the variables within 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 patte ...
CISB450 - Department of Computer and Information Science
... Elective course in Computer Science Course description: (2-2) 3 credits. This course introduces key concepts of artificial intelligence and application areas. Topics include expert systems, computational intelligence, machine learning, genetic algorithms, and clustering. Upon completion of this cour ...
... Elective course in Computer Science Course description: (2-2) 3 credits. This course introduces key concepts of artificial intelligence and application areas. Topics include expert systems, computational intelligence, machine learning, genetic algorithms, and clustering. Upon completion of this cour ...
Learning: Not Just the Facts, Ma`am, but the
... that could have happened but did not. Such counterfactual outcomes can influence our choices and hasten learning. A series of recent studies has begun to untangle the neural circuitry responsible for monitoring counterfactual outcomes. Here, we summarize several recent complementary discoveries, inc ...
... that could have happened but did not. Such counterfactual outcomes can influence our choices and hasten learning. A series of recent studies has begun to untangle the neural circuitry responsible for monitoring counterfactual outcomes. Here, we summarize several recent complementary discoveries, inc ...
The neural network model of music cognition ARTIST and
... classification takes place. The role of the learning algorithm is to create categories in F2 when it is needed -when an input is sufficiently different from what has been learned so far and does not fit in any exististing category-and to find an optimal set of synaptic weights for a meaningful categ ...
... classification takes place. The role of the learning algorithm is to create categories in F2 when it is needed -when an input is sufficiently different from what has been learned so far and does not fit in any exististing category-and to find an optimal set of synaptic weights for a meaningful categ ...
Towards the Theory-Guided Design of Help
... knowledge. In accordance with ISPDL Theory, the state model contains knowledge acquired by heuristics (not in the state model) and knowledge optimized by composition. • Concerning design principle 5: We work on a process model to simulate two single subjects´ knowledge acquisition, impasses, and su ...
... knowledge. In accordance with ISPDL Theory, the state model contains knowledge acquired by heuristics (not in the state model) and knowledge optimized by composition. • Concerning design principle 5: We work on a process model to simulate two single subjects´ knowledge acquisition, impasses, and su ...
Learning and Predicting Dynamic Network Behavior with Graphical
... There has been much interest in modeling and analyzing social behaviors that facilitate the diffusion of information in various online network scenarios. Recommendation network studies for example have identified node-centric and network-related factors dictating the spread of recommendations on vid ...
... There has been much interest in modeling and analyzing social behaviors that facilitate the diffusion of information in various online network scenarios. Recommendation network studies for example have identified node-centric and network-related factors dictating the spread of recommendations on vid ...
An Introduction to Deep Learning
... The main idea of convolutional networks is to combine local computations (convolution of the signal with weight sharing units) and pooling. The convolutions are intended to give translation invariance to the system, as the weights depend only on spatial separation and not on spatial position. The po ...
... The main idea of convolutional networks is to combine local computations (convolution of the signal with weight sharing units) and pooling. The convolutions are intended to give translation invariance to the system, as the weights depend only on spatial separation and not on spatial position. The po ...
Reinforcement learning in populations of spiking neurons
... The role of neuronal populations in encoding sensory stimuli has been intensively studied1,2. However, most models of reinforcement learning with spiking neurons have focused on just single neurons or small neuronal assemblies3–6. Furthermore, the following result indicates that such models do not s ...
... The role of neuronal populations in encoding sensory stimuli has been intensively studied1,2. However, most models of reinforcement learning with spiking neurons have focused on just single neurons or small neuronal assemblies3–6. Furthermore, the following result indicates that such models do not s ...
A Knowledge Representation Tool for Autonomous Machine
... Knowledge representation is centered by the model of concepts, which is a primary element in cognitive informatics and a basic unit of thought and reasoning. Concept is an abstract model of tangible or intangible entities. Concept algebra developed by Yingxu Wang is an abstract mathematical structur ...
... Knowledge representation is centered by the model of concepts, which is a primary element in cognitive informatics and a basic unit of thought and reasoning. Concept is an abstract model of tangible or intangible entities. Concept algebra developed by Yingxu Wang is an abstract mathematical structur ...
Descision making
... The gain (or reward) a monkey can expect to realize from an eyemovement response modulates the activity of neurons in the lateral intraparietal area (LIP). In Addition, the activity of these neurons is sensitive to the probability that a particular response will result in a gain. ...
... The gain (or reward) a monkey can expect to realize from an eyemovement response modulates the activity of neurons in the lateral intraparietal area (LIP). In Addition, the activity of these neurons is sensitive to the probability that a particular response will result in a gain. ...
artificial intelligence fellows program
... data science teams, which focus on asking the right questions and extracting meaningful information from data to make decisions or build data products. AI teams usually focus on building, optimizing, and scaling deep learning algorithms that emulate core human abilities such as vision, speech, langu ...
... data science teams, which focus on asking the right questions and extracting meaningful information from data to make decisions or build data products. AI teams usually focus on building, optimizing, and scaling deep learning algorithms that emulate core human abilities such as vision, speech, langu ...
neuron models and basic learning rules
... Produced by Qiangfu Zhao (Sine 1997), All rights reserved © ...
... Produced by Qiangfu Zhao (Sine 1997), All rights reserved © ...
Fast Parameter Learning for Markov Logic Networks Using Bayes Nets
... learning in Markov logic is a convex optimization problem, and thus gradient descent is guaranteed to find the global optimum. However, convergence to this optimum may be extremely slow, partly because the problem is ill-conditioned since different clauses may have very different numbers of satisfyi ...
... learning in Markov logic is a convex optimization problem, and thus gradient descent is guaranteed to find the global optimum. However, convergence to this optimum may be extremely slow, partly because the problem is ill-conditioned since different clauses may have very different numbers of satisfyi ...
Categories - Widodo.com
... A fixed set of classes: C = {c1, c2,…, cJ} A training set D of documents each with a label in C Determine: A learning method or algorithm which will enable us to learn a classifier γ For a test document d, we assign it the class γ(d) ∈ C ...
... A fixed set of classes: C = {c1, c2,…, cJ} A training set D of documents each with a label in C Determine: A learning method or algorithm which will enable us to learn a classifier γ For a test document d, we assign it the class γ(d) ∈ C ...
Highlights of Hinton`s Contrastive Divergence Pre
... • Supervised training of deep models (e.g. manylayered NNets) is difficult (optimization problem) • Shallow models (SVMs, one-hidden-layer NNets, boosting, etc…) are unlikely candidates for learning high-level abstractions needed for AI • Unsupervised learning could do “local-learning” (each module ...
... • Supervised training of deep models (e.g. manylayered NNets) is difficult (optimization problem) • Shallow models (SVMs, one-hidden-layer NNets, boosting, etc…) are unlikely candidates for learning high-level abstractions needed for AI • Unsupervised learning could do “local-learning” (each module ...
Cognitive Architectures: Where do we go from here?
... What should be required from an AI system to be worthy of the “Artificial General Intelligence” name? Artificial Intelligence has focused on many specific approaches to problem solving, useful for development of expert systems, neglecting its initial ambitious goals. One requirement for AGI, storing ...
... What should be required from an AI system to be worthy of the “Artificial General Intelligence” name? Artificial Intelligence has focused on many specific approaches to problem solving, useful for development of expert systems, neglecting its initial ambitious goals. One requirement for AGI, storing ...
Informed Initial Policies for Learning in Dec
... Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multi-agent systems where agents operate with noisy sensors and actuators and local information. While many techniques have been developed for solving DecPOMDPs exactly and appr ...
... Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multi-agent systems where agents operate with noisy sensors and actuators and local information. While many techniques have been developed for solving DecPOMDPs exactly and appr ...
Assessing Conceptual Similarity to Support Concept Mapping б г д
... Upper Nodes: Concepts that appear towards the top of the map in its graphical representation. Lower Nodes: Concepts that appear towards the bottom of the concept map in its graphical representation. Our algorithms to compute these weights are adapted from research on determining hub and authorities ...
... Upper Nodes: Concepts that appear towards the top of the map in its graphical representation. Lower Nodes: Concepts that appear towards the bottom of the concept map in its graphical representation. Our algorithms to compute these weights are adapted from research on determining hub and authorities ...
Reinforcement Learning: Dynamic Programming
... Asynchronous Value Iteration: Every time-step update only a few states AsyncVI Theorem: If all states are updated infinitely often, the algorithm converges to V*. How to use? Prioritized Sweeping IPS [MacMahan & Gordon ’05]: Instead of an update, put state on the priority queue When picking ...
... Asynchronous Value Iteration: Every time-step update only a few states AsyncVI Theorem: If all states are updated infinitely often, the algorithm converges to V*. How to use? Prioritized Sweeping IPS [MacMahan & Gordon ’05]: Instead of an update, put state on the priority queue When picking ...
A Synapse Plasticity Model for Conceptual Drift Problems Ashwin Ram ()
... propagation. Propagation of signals can be expressed in terms of the synapses and their relation to the soma. For each synapse, the distance from the presynaptic soma to the synapse represents the time for an action potential to propagate from the presynaptic soma to the synapse. Likewise, the laten ...
... propagation. Propagation of signals can be expressed in terms of the synapses and their relation to the soma. For each synapse, the distance from the presynaptic soma to the synapse represents the time for an action potential to propagate from the presynaptic soma to the synapse. Likewise, the laten ...