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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Fall 2022
With the increase in the number of deep learning networks, many excellent methods have been proposed for video segmentation tasks. However, most of the these methods are for learning pattern information. Not as much work has been done in the area of distribution information, which is also useful...
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Spring 2019
In this thesis we introduce a new loss for regression, the Histogram Loss. There is some evidence that, in the problem of sequential decision making, estimating the full distribution of return offers a considerable gain in performance, even though only the mean of that distribution is used in...
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Do Inputs Matter? Using Data-Dependence Profiling to Evaluate Thread Level Speculation in the BlueGene/Q
DownloadFall 2013
In the era of many-core architectures, it is necessary to fully exploit the maximum available parallelism in computer programs. Thread Level Speculation (TLS) is a hardware/software technique that guarantees correct speculative parallel execution of the program even in the presence of may...
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Fall 2023
Recent work has shown that by approximating the behaviour of a non-differentiable black-box function using a neural network, the black-box can be integrated into a differentiable training pipeline for end-to-end training. This methodology is termed "differentiable bypass," and a successful...
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Spring 2019
We examine various techniques in SAT-based (Satisfiability) planningand explore how they can be applied and further improved in the contextof ASP (Answer Set Programming). First, we look at the 2006 plannerSATPlan and show that their encoding, when translated directly intoASP, enjoys a...
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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...