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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 2Rabbany khorasgani, Reihaneh
- 2Sacharuk, Edward, 1948-
- 2Sharifi, AmirAli
- 54Machine Learning
- 48Reinforcement Learning
- 37Artificial Intelligence
- 31Machine learning
- 20Image processing. Digital techniques.
- 18Artificial intelligence
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...
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Spring 2022
The concept of state is fundamental to a reinforcement learning agent. The state is the input to the agent's action-selection policy, value functions, and environmental model. A reinforcement learning agent interacts with the environment by performing actions and receiving observations, resulting...
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Mixed Low-bit Quantization for Model Compression with Layer Importance and Gradient Estimations
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Deep neural networks (DNNs) have been widely used in the modern world in recent years. However, due to the substantial memory consumption and high computational power use of DNNs, deploying them on devices with limited resources is challenging. Model compression methods can provide us with a...
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Spring 2022
Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...
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Building an expert-system based conversational agent to provide personalised resources about neurological disorders
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Researchers developing artificially intelligent conversational agents (aka, chat- bots) seek effective ways to provide personal assistance to users with various needs. We have implemented a web-based conversational agent that recom- mends resources to help clients (caregivers of patients...
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Spring 2022
Automated Vehicle (AV) is a trending technology being developed with the promise to reduce traffic accidents caused by human errors. Perception plays a crucial role for Automated Driving Systems (ADS) to make safe decisions. However, local sensory data is insufficient to capture comprehensive...
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Sample-Efficient Control with Directed Exploration in Discounted MDPs Under Linear Function Approximation
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An important goal of online reinforcement learning algorithms is efficient data collection to learn near-optimal behaviour, that is, optimizing the exploration-exploitation trade-off to reduce the sample-complexity of learning. To improve sample-complexity of learning it is essential that the...
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Spring 2022
Computational lexical semantics is the study of word meanings which involves algorithms and ontologies. Computation of semantic similarity plays an important role in various applications of natural language processing, including information retrieval, machine translation, and question answering....
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Fall 2022
Overfitting is a phenomenon when a machine learning system learns the patterns in training data so well that it starts to inauspiciously affect the model performance on unseen data. In practice, machine learning systems that overfit are not deployable rather systems that generalize well and do...
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Fall 2022
The motivation to incorporate planning, temporal abstraction and value function approximation in reinforcement learning (RL) algorithms is to reduce the amount of interaction with the environment needed to learn a near-optimal policy. Although each of these concepts has been under intense...