Chapter
... Multidimensional analysis tools Digital dashboards Statistical tools GISs Our remaining Specialized analytics focus Artificial intelligence ...
... Multidimensional analysis tools Digital dashboards Statistical tools GISs Our remaining Specialized analytics focus Artificial intelligence ...
Learning Markov Networks With Arithmetic Circuits
... of features [14], which may have high treewidth but still admits efficient inference. However, this approach leads to many very long features, with lengths proportional to the depth of the tree. Another method for learning tractable graphical models is to use mixture models with latent variables. Th ...
... of features [14], which may have high treewidth but still admits efficient inference. However, this approach leads to many very long features, with lengths proportional to the depth of the tree. Another method for learning tractable graphical models is to use mixture models with latent variables. Th ...
goto report
... algorithm can be used to find the best combination of these structures to beat the player. A player would go through a level of the game and at the end, the program would pick the monsters that fared the best against the player, and use those in the next generation. Slowly, after a lot of playing, s ...
... algorithm can be used to find the best combination of these structures to beat the player. A player would go through a level of the game and at the end, the program would pick the monsters that fared the best against the player, and use those in the next generation. Slowly, after a lot of playing, s ...
Decentralized reinforcement learning control of a robotic manipulator
... speed might be higher for decentralized learners. This is because each agent i searches an action space Ui . A centralized learner solving the same problem searches the joint action space U = U1 × · · · × Un , which is exponentially larger. This difference will be even more significant in tasks wher ...
... speed might be higher for decentralized learners. This is because each agent i searches an action space Ui . A centralized learner solving the same problem searches the joint action space U = U1 × · · · × Un , which is exponentially larger. This difference will be even more significant in tasks wher ...
CIS 730 (Introduction to Artificial Intelligence) Lecture
... – If b is a final board state that is won, then V(b) = 100 – If b is a final board state that is lost, then V(b) = -100 – If b is a final board state that is drawn, then V(b) = 0 – If b is not a final board state in the game, then V(b) = V(b’) where b’ is the best final board state that can be achie ...
... – If b is a final board state that is won, then V(b) = 100 – If b is a final board state that is lost, then V(b) = -100 – If b is a final board state that is drawn, then V(b) = 0 – If b is not a final board state in the game, then V(b) = V(b’) where b’ is the best final board state that can be achie ...
Shivani Agarwal
... where 1(φ) is 1 if φ is true and 0 otherwise. In order to design a good ranking function f , we need to estimate the error incurred by f on new movies. Under suitable probabilistic assumptions, we would like to find out whether we can use the observed or empirical error of f on the movies for which ...
... where 1(φ) is 1 if φ is true and 0 otherwise. In order to design a good ranking function f , we need to estimate the error incurred by f on new movies. Under suitable probabilistic assumptions, we would like to find out whether we can use the observed or empirical error of f on the movies for which ...
NeuralNets
... Hill-Climbing in Multi-Layer Nets • Since “greed is good” perhaps hill-climbing can be used to learn multi-layer networks in practice although its theoretical limits are clear. • However, to do gradient descent, we need the output of a unit to be a differentiable function of its input and weights. ...
... Hill-Climbing in Multi-Layer Nets • Since “greed is good” perhaps hill-climbing can be used to learn multi-layer networks in practice although its theoretical limits are clear. • However, to do gradient descent, we need the output of a unit to be a differentiable function of its input and weights. ...
Slides - School of Computer Science
... The incredibly rapid growth and increasing pervasiveness of the Internet brings to mind a piece of science fiction, a short story, that I read many years ago in the days when UNIVACs and enormous IBM mainframes represented the popular image of computers. In the story, […] While such an exaggerated ...
... The incredibly rapid growth and increasing pervasiveness of the Internet brings to mind a piece of science fiction, a short story, that I read many years ago in the days when UNIVACs and enormous IBM mainframes represented the popular image of computers. In the story, […] While such an exaggerated ...
A bio-inspired learning signal for the cumulative learning - laral
... functional role of DA signal. One hipothesis[14–16] looks at the similarities of DA activations with the temporal-difference (TD) error of computational reinforcement learning [17], and suggests that phasic DA represents a reward prediction error signal with the role of guiding the maximisation of ...
... functional role of DA signal. One hipothesis[14–16] looks at the similarities of DA activations with the temporal-difference (TD) error of computational reinforcement learning [17], and suggests that phasic DA represents a reward prediction error signal with the role of guiding the maximisation of ...
ling411-19-Learning - OWL-Space
... between cortical neurons or columns For neurons of neighboring columns: 1 For distant neurons in same hemisphere • Range: 1 to about 5 or 6 (estimate) • Mostly 1, 2, or 3, especially if functionally closely related • Average about 3 (estimate) For opposite hemisphere • Add 1 to figures for sam ...
... between cortical neurons or columns For neurons of neighboring columns: 1 For distant neurons in same hemisphere • Range: 1 to about 5 or 6 (estimate) • Mostly 1, 2, or 3, especially if functionally closely related • Average about 3 (estimate) For opposite hemisphere • Add 1 to figures for sam ...
Zheng Chen - Washington University in St. Louis
... • Implemented Hadoop-based algorithms to efficiently extract discriminative features (e.g. local clustering coefficient) for bots detection from terabytes of Twitter Streaming API data. • Developed an integrative web-based system to interactively rank, label, and detect social bots from millions of ...
... • Implemented Hadoop-based algorithms to efficiently extract discriminative features (e.g. local clustering coefficient) for bots detection from terabytes of Twitter Streaming API data. • Developed an integrative web-based system to interactively rank, label, and detect social bots from millions of ...
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Case-based reasoning foundations
... case adaptation and learning of new cases are applied. The variety of ways in which CBR systems were developed in the first ten years of the field is described in Kolodner’s (1993) CBR text book. Despite the many different appearances of CBR systems the essentials of CBR is captured in a surprisingly ...
... case adaptation and learning of new cases are applied. The variety of ways in which CBR systems were developed in the first ten years of the field is described in Kolodner’s (1993) CBR text book. Despite the many different appearances of CBR systems the essentials of CBR is captured in a surprisingly ...
A differentiable approach to inductive logic programming
... Inductive logic programming (ILP) [1] refers to a broad class of problems that aim to find logic rules that model the observed data. The observed data usually contains background knowledge and examples, typically in the form of database relations or knowledge graphs. Inductive logic programming is o ...
... Inductive logic programming (ILP) [1] refers to a broad class of problems that aim to find logic rules that model the observed data. The observed data usually contains background knowledge and examples, typically in the form of database relations or knowledge graphs. Inductive logic programming is o ...
Machine learning
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition ""can be viewed as two facets ofthe same field.""When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.