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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Spring 2024
The representation of real-world relationships and entities through nodes and edges in a network has found wide applicability across diverse scientific fields. At the core of network analysis are the tasks of community detection and community search, which aim to identify distinct groups within a...
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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A Study of the Efficacy of Generative Flow Networks for Robotics and Machine Fault-Adaptation
DownloadSpring 2024
In 2005, Opportunity, one of NASA’s renowned Mars rovers, faced a dire situation. It was a moment that could end a mission that had already far outlasted its expected lifespan. After clambering out of the Victoria crater, the rover started to experience an abrupt current spike in its right front...
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Investigating Feature Importance In Educational Data, Towards Handling Data Missingness in Classification Tasks
DownloadSpring 2024
The problem of missing data is unavoidable in many research fields, especially in education where data can be missing for justifiable reasons. Missing data causes bias in analysis, and traditional methods like complete case analysis and single imputation are suboptimal yet typically used to...
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Fall 2024
This thesis presents a novel data-driven approach for identifying categoryselective regions in the human brain that are consistent across multiple participants. By leveraging a massive fMRI dataset and a multi-modal (language and image) neural network (CLIP), we trained a highly accurate...
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Fall 2024
Metrics for problem difficulty are used by many puzzle generation algorithms, as well as by adaptive algorithms that are expected to provide players with the puzzles at the correct level of difficulty. A recently proposed general metric, puzzle entropy, combines an analysis of game mechanics with...
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The Contrastive Gap: A New Perspective on the ‘Modality Gap’ in Multimodal Contrastive Learning
DownloadFall 2024
Learning jointly from images and texts using contrastive pre-training has emerged as an effective method to train large-scale models with a strong grasp of semantic image concepts. For instance, CLIP, pre-trained on a large corpus of web data, excels in tasks like zero-shot image classification,...
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Fall 2024
QUIC has been a fast-evolving protocol and, with its standardization as part of HTTP/3, it is an important part of the World Wide Web. Since its introduction in 2014, QUIC changed significantly from Google QUIC (gQUIC) to an IETF standard (2021). Understanding the performance of the current...
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Fall 2024
Procedural Content Generation via Machine Learning (PCGML) faces a significant hurdle that sets it apart from other ML problems, such as image or text generation, which is limited annotated data. For example, many existing methods for level generation via machine learning specifically require a...
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Improving Breast Cancer Biomarker Predictors from Morphology via Corresponding Gaussian Processes
DownloadFall 2024
Hosseini Akbarnejad, Amir Hossein
Biomarkers for cancer are tests performed on tumoral tissue which extract information from genes (DNA, deoxyribonucleic acid), product of genes (RNA, ribonucleic acid) and proteins. The information obtained from biomarkers (abnormal amount, strutural defect, etc.) is the basis for breast cancer...