Search
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
-
Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
DownloadFall 2022
In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
-
Spring 2010
Domain-independent feature learning is a hard problem. This is reflected by lack of broad research in the area. The goal of General Game Playing (GGP) can be described as designing computer programs that can play a variety of games given only a logical game description. Any learning has to be...
-
Fall 2023
Federated learning is in widespread use for learning a global model when data is distributed across various distributed clients. In much of the prior work, the data is assumed to consist of independent data points. However, there is often an underlying graph that structures the data points. Such...
-
Finding reliable resources and Chatting with Mira while considering emotions when the scenario is unscripted
DownloadSpring 2024
The primary wellspring of information, the Internet, abounds with misinfor- mation, particularly in the domains of mental health and psychology. This also affects the reliability and truth of responses of chatbots counting on unverified data. To address this concern, the MIRA project started to...
-
Fall 2014
We address the problem of finding ‘surprising’ patterns of variable length in sequence data, where a surprising pattern is defined as a subsequence of a longer sequence, whose observed frequency is statistically significant with respect to a given distribution.Finding statistically significant...
-
Fall 2014
Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major clustering paradigms: density-based and semi-supervised clustering. Density-based clustering algorithms seek partitions with high-density areas of...
-
Fixed Point Propagation: A New Way To Train Recurrent Neural Networks Using Auxiliary Variables
DownloadFall 2019
Recurrent neural networks (RNNs), along with their many variants, provide a powerful tool for online prediction in partially observable problems. Two issues concerning RNNs, however, are the ability to capture long-term dependencies and long training times. There have been a variety of strategies...