Learning Dynamic Text Embedding Models Using CNNs


Learning Dynamic Text Embedding Models Using CNNs – In this paper, we present a new neural network based system architecture that combines the advantages of CNN-style reinforcement learning and reinforcement learning to solve the task-solving challenge of visual retrieval. With the proposed approach, we have achieved a speed-up of more than 10 times with a linear classification error rate of 1.22% without any supervision.

While machine learning (ML) models recently led to remarkable successes in many tasks, the use of ML has not been widely investigated in the reinforcement learning (RL) community. A key challenge in RL is the problem of representing the rewards of the actions as inputs to the learning algorithm, which often assumes that the RL algorithm is a continuous model that provides rewards for all actions. To alleviate this problem, we propose a novel RL algorithm with a finite set of actions. Using the RL algorithm, which is shown to be robust to adversarial inputs, we construct new RL algorithms that are able to learn to produce outputs that are qualitatively different from the inputs to the RL algorithm. Experiments on two standard benchmarks on both human and machine RL examples show that the RL algorithm compares favorably with the state of the art RL algorithms on several tasks over the time span of two years.

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Learning Dynamic Text Embedding Models Using CNNs

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  • Variational Inference via the Gradient of Finite Domains

    A new metaheuristic for optimal reinforcement learning algorithm exploiting a classical financial optimization equationWhile machine learning (ML) models recently led to remarkable successes in many tasks, the use of ML has not been widely investigated in the reinforcement learning (RL) community. A key challenge in RL is the problem of representing the rewards of the actions as inputs to the learning algorithm, which often assumes that the RL algorithm is a continuous model that provides rewards for all actions. To alleviate this problem, we propose a novel RL algorithm with a finite set of actions. Using the RL algorithm, which is shown to be robust to adversarial inputs, we construct new RL algorithms that are able to learn to produce outputs that are qualitatively different from the inputs to the RL algorithm. Experiments on two standard benchmarks on both human and machine RL examples show that the RL algorithm compares favorably with the state of the art RL algorithms on several tasks over the time span of two years.


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