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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 2Rabbany khorasgani, Reihaneh
- 2Sacharuk, Edward, 1948-
- 2Sharifi, AmirAli
- 68Machine Learning
- 63Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 21Natural Language Processing
- 20Image processing. Digital techniques.
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Fall 2021
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous,...
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Fall 2021
Structural credit assignment in neural networks is a long-standing problem, with a variety of alternatives to backpropagation proposed to allow for local training of nodes. One of the early strategies was to treat each node as an agent and use a reinforcement learning method called REINFORCE to...
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Fall 2021
Reinforcement learning (RL) is a powerful way of solving sequential decision-making tasks in which the agent’s goal is to learn how to maximize its reward.RL approaches can be divided into 2 different categories: model-based approaches that learn with the help of a model of the environment,...
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Fall 2021
A large portion of quantitative information about entities mentioned in Web pages is expressed as Web tables, and these tables often lack proper schema and annotation, which introduces challenges for the purpose of querying and further analysis. In this thesis, we study the problem of annotating...
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Spring 2021
This thesis evaluated Convoultional LSTM (ConvLSTM) for frame prediction to help better understand motion in neural networks. Three different neural networks were implemented and trained. The three networks included, the original ConvLSTM paper by Shi et al. [35], the Spatio-Temporal network by...