Learning Macro-Actions in Reinforcement Learning
... In reinforcement learning we want to learn a mapping from states to actions, s -+ a that maximizes the total expected reward (Sutton & Barto, 1998). Sometimes it might be of use to learn a mapping from actions to actions as well. We believe that acting according to an action-to-action mapping can be ...
... In reinforcement learning we want to learn a mapping from states to actions, s -+ a that maximizes the total expected reward (Sutton & Barto, 1998). Sometimes it might be of use to learn a mapping from actions to actions as well. We believe that acting according to an action-to-action mapping can be ...
Power 3
... • Example: throw two dice: event is white die equals one • example: throw two dice and red die equals one • example: throw two dice and the sum is ...
... • Example: throw two dice: event is white die equals one • example: throw two dice and red die equals one • example: throw two dice and the sum is ...
Deep learning with COTS HPC systems
... 1 billion trainable parameters. While such extremely large networks are potentially valuable objects of AI research, the expense to train them is overwhelming: the distributed computing infrastructure (known as “DistBelief”) used for the experiments in (Le et al., 2012) manages to train a neural net ...
... 1 billion trainable parameters. While such extremely large networks are potentially valuable objects of AI research, the expense to train them is overwhelming: the distributed computing infrastructure (known as “DistBelief”) used for the experiments in (Le et al., 2012) manages to train a neural net ...
Dynamic Programming and Graph Algorithms in Computer Vision
... survey is inherently somewhat selective, and there are a number of related topics that we have omitted. We chose to focus on a dynamic programming and on graph algorithms, since they share two key properties: first, they draw on a body of well-established, closely inter-related techniques, which are ...
... survey is inherently somewhat selective, and there are a number of related topics that we have omitted. We chose to focus on a dynamic programming and on graph algorithms, since they share two key properties: first, they draw on a body of well-established, closely inter-related techniques, which are ...
Slides(II) - Zhangxi Lin
... Seems there are many things are on going. How to handle them properly in the ...
... Seems there are many things are on going. How to handle them properly in the ...
Calibration of Electrical Fast Transient/ Burst Generators
... The second part of the SCL software tools is an Excel user-defined function (UDF) that can be embedded in any Excel worksheet for GUF or MCM computation. An Excel UDF behaves just like other Excel built-in functions. It takes arguments and returns a value. An Excel UDF will be executed whenever any ...
... The second part of the SCL software tools is an Excel user-defined function (UDF) that can be embedded in any Excel worksheet for GUF or MCM computation. An Excel UDF behaves just like other Excel built-in functions. It takes arguments and returns a value. An Excel UDF will be executed whenever any ...
Algorithm selection by rational metareasoning as
... can be binary (correct vs. incorrect output) or numeric (e.g., error penalty). The selection mapping m defined in Equation 2 depends on the conditional distributions of score and runtime (P (S|a, i) and P (T |a, i)). These distributions are generally unknown, but they can be learned. Learning an app ...
... can be binary (correct vs. incorrect output) or numeric (e.g., error penalty). The selection mapping m defined in Equation 2 depends on the conditional distributions of score and runtime (P (S|a, i) and P (T |a, i)). These distributions are generally unknown, but they can be learned. Learning an app ...
Normalizing and Redistributing Variables
... occur with a certain pattern and ignoring these instances removes samples according to a pattern introducing distortion to the sample. ...
... occur with a certain pattern and ignoring these instances removes samples according to a pattern introducing distortion to the sample. ...
Handling missing values in Analysis
... Handling missing values in Analysis Before we analyze the data, which includes missing values, we should make sure that all the missing values have been coded as SAS missing values. There are many ways to code missing data in SAS. The mostly used is Missing numeric data: a single period(.); Missing ...
... Handling missing values in Analysis Before we analyze the data, which includes missing values, we should make sure that all the missing values have been coded as SAS missing values. There are many ways to code missing data in SAS. The mostly used is Missing numeric data: a single period(.); Missing ...
IJAI-6 - aut.upt.ro
... Sentiment classification aims to distinguish whether people like or dislike a product, a service, an organization, an individual or a topic. Sentiment classification for product reviews has recently attracted much attention from the natural language processing community. Researchers have investigate ...
... Sentiment classification aims to distinguish whether people like or dislike a product, a service, an organization, an individual or a topic. Sentiment classification for product reviews has recently attracted much attention from the natural language processing community. Researchers have investigate ...
wang97geneticbased.pdf
... Due to deformations, some structural information of the graph may be missed or distorted. The matching problem in finding the vertex mapping for two nonisomorphic graphs becomes the error-correcting graph isomorphism (ECGI) problem. An example illustrating the ECGI problem is shown in Fig. 2. It is ...
... Due to deformations, some structural information of the graph may be missed or distorted. The matching problem in finding the vertex mapping for two nonisomorphic graphs becomes the error-correcting graph isomorphism (ECGI) problem. An example illustrating the ECGI problem is shown in Fig. 2. It is ...
Introduction to Artificial Intelligence (Undergraduate Topics in
... sciences and, for the most part, requires high school level knowledge of mathematics. In several places, knowledge from linear algebra and multidimensional analysis is needed. For a deeper understanding of the contents, actively working on the exercises is indispensable. This means that the solution ...
... sciences and, for the most part, requires high school level knowledge of mathematics. In several places, knowledge from linear algebra and multidimensional analysis is needed. For a deeper understanding of the contents, actively working on the exercises is indispensable. This means that the solution ...
An Annotated Dataset for Extracting Definitions and Hypernyms from
... relies on a large set of machine-learned, but “fixed” lexicosyntactic patterns. Cui et al. (2007) propose the use of probabilistic patterns, called soft patterns, for definitional question answering in the TREC contest2 . Soft patterns generalize over lexico-syntactic “hard” (fixed) patterns in that ...
... relies on a large set of machine-learned, but “fixed” lexicosyntactic patterns. Cui et al. (2007) propose the use of probabilistic patterns, called soft patterns, for definitional question answering in the TREC contest2 . Soft patterns generalize over lexico-syntactic “hard” (fixed) patterns in that ...
artificial neural networks
... Information is stored and processed in a neural network simultaneously throughout the whole network, rather than at specific locations. In other words, in neural networks, both data and its processing are global rather than local. Learning is a fundamental and essential characteristic of biological ...
... Information is stored and processed in a neural network simultaneously throughout the whole network, rather than at specific locations. In other words, in neural networks, both data and its processing are global rather than local. Learning is a fundamental and essential characteristic of biological ...
Basic Rules of Combining Probability
... - Addition Rule: Case two: Not mutually exclusive events: there can be overlap between them. The probability of overlap must be subtracted from the sum of probabilities of the separate events. A ...
... - Addition Rule: Case two: Not mutually exclusive events: there can be overlap between them. The probability of overlap must be subtracted from the sum of probabilities of the separate events. A ...
Supporting Educational Loan Decision Making Using
... Nodes are used to represent the brain’s neurons and these nodes are connected to each other in layers of processing. Fig. 1 illustrates the three types of layers of nodes: the input layer, the hidden layer or layers (representing the synapses) and the output layer. The input layer contains data fro ...
... Nodes are used to represent the brain’s neurons and these nodes are connected to each other in layers of processing. Fig. 1 illustrates the three types of layers of nodes: the input layer, the hidden layer or layers (representing the synapses) and the output layer. The input layer contains data fro ...
Steel Production and Its Uses
... out the inputs x' which correspond to a given output y'. This means the output space will now be mapped to the input space instead of being mapped from the input space. However the problem associated with this representation is that this mapping will result in a one to many mapping between the outpu ...
... out the inputs x' which correspond to a given output y'. This means the output space will now be mapped to the input space instead of being mapped from the input space. However the problem associated with this representation is that this mapping will result in a one to many mapping between the outpu ...
AI-Complete CAPTCHAs - Computer Engineering and Computer
... “NP-complete”] adj. Used to describe problems or subproblems in AI, to indicate that the solution presupposes a solution to the “strong AI problem” (i.e., the synthesis of a human-level intelligence). A problem that is AI-complete is, in other words, just too hard. Examples of AI-complete problems a ...
... “NP-complete”] adj. Used to describe problems or subproblems in AI, to indicate that the solution presupposes a solution to the “strong AI problem” (i.e., the synthesis of a human-level intelligence). A problem that is AI-complete is, in other words, just too hard. Examples of AI-complete problems a ...
