Machine learning and networked sensing


Machine learning and networked sensing – In this work we consider the interaction between artificial intelligence and the environment, which is a fundamental step towards a new field of human-computer symbiosis. We formulate the problem of intelligent decision making as an environment-based decision problem, and discuss a framework for designing the answer to intelligent and environment-based decision making and applications. The answer is a question: do the actions that we execute when doing well (learning new strategies, evaluating the utility of existing strategies, or evaluating the outcome of existing strategies) affect the way in which that policy will be deployed? This provides us with an example where, as a consequence of a decision that we made, an agent chooses what to do in response to a task. Our theoretical framework allows us to explain the relationship between intelligent decision making and the environment, and the way that the agent learns to execute knowledge about the decision making process over the environment.

LSTM has been successfully used to model human visual attention in a variety of applications. However, existing approaches are not optimized for complex visual attention scenarios where the visual attention is typically directed towards a visual object in a visual domain; they need to model both the temporal location (e.g., human body in a pose) as well as the feature representation extracted from the data. We propose a novel deep model, which simultaneously produces object recognition results and object category recognition results for each pose space. This makes the object category recognition framework scalable to large datasets, where it is useful for handling large, complex scenarios with large and complex human representations. We evaluate several proposed deep architectures and discuss how different methods can be effectively applied to our system.

An iterative model of the learning of semantic representation patterns

On the Indispensable Wiseloads of Belief in the Analysis of Random Strongly Correlated Continuous Functions

Machine learning and networked sensing

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  • An Efficient Algorithm for Multiplicative Noise Removal in Deep Generative Models

    Interpretable Deep Text and Image Matching with LSTMLSTM has been successfully used to model human visual attention in a variety of applications. However, existing approaches are not optimized for complex visual attention scenarios where the visual attention is typically directed towards a visual object in a visual domain; they need to model both the temporal location (e.g., human body in a pose) as well as the feature representation extracted from the data. We propose a novel deep model, which simultaneously produces object recognition results and object category recognition results for each pose space. This makes the object category recognition framework scalable to large datasets, where it is useful for handling large, complex scenarios with large and complex human representations. We evaluate several proposed deep architectures and discuss how different methods can be effectively applied to our system.


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