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- Deep Learning (2)
- Bayesian Networks (1)
- Dark protein (1)
- Decision Making (1)
- Discretization (1)
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- Drug Discovery (1)
- Drug discovery (1)
- Explainability (1)
- Graph Neural Networks (1)
- Grapheme-phoneme mapping (1)
- Knowledge Discovery (1)
- Machine Learning (1)
- Mixed Missing Temporal Data (1)
- Morphological inflection (1)
- Neural model (1)
- Out-of-Distribution generalization (1)
- Outcome Uncertainty (1)
- Prediction (1)
- Preterm Birth Prediction (1)
- Quasi-regularity (1)
- Unsupervised Machine Learning (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai
Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai
Dissertations, Theses, and Capstone Projects
Dark protein illumination is a fundamental challenge in drug discovery where majority human proteins are understudied, i.e. with only known protein sequence but no known small molecule binder. It's a major road block to enable drug discovery paradigm shift from single-targeted which looks to identify a single target and design drug to regulate the single target to multi-targeted in a Systems Pharmacology perspective. Diseases such as Alzheimer's and Opioid-Use-Disorder plaguing millions of patients call for effective multi-targeted approach involving dark proteins. Using limited protein data to predict dark protein property requires deep learning systems with OOD generalization capacity. Out-of-Distribution (OOD) …
Optimization And Application Of Graph Neural Networks, Shuo Zhang
Optimization And Application Of Graph Neural Networks, Shuo Zhang
Dissertations, Theses, and Capstone Projects
Graph Neural Networks (GNNs) are widely recognized for their potential in learning from graph-structured data and solving complex problems. However, optimal performance and applicability of GNNs have been an open-ended challenge. This dissertation presents a series of substantial advances addressing this problem. First, we investigate attention-based GNNs, revealing a critical shortcoming: their ignorance of cardinality information that impacts their discriminative power. To rectify this, we propose Cardinality Preserved Attention (CPA) models that can be applied to any attention-based GNNs, which exhibit a marked improvement in performance. Next, we introduce the Directional Node Pair (DNP) descriptor and the Robust Molecular Graph …
Evaluating Neural Networks As Cognitive Models For Learning Quasi-Regularities In Language, Xiaomeng Ma
Evaluating Neural Networks As Cognitive Models For Learning Quasi-Regularities In Language, Xiaomeng Ma
Dissertations, Theses, and Capstone Projects
Many aspects of language can be categorized as quasi-regular: the relationship between the inputs and outputs is systematic but allows many exceptions. Common domains that contain quasi-regularity include morphological inflection and grapheme-phoneme mapping. How humans process quasi-regularity has been debated for decades. This thesis implemented modern neural network models, transformer models, on two tasks: English past tense inflection and Chinese character naming, to investigate how transformer models perform quasi-regularity tasks. This thesis focuses on investigating to what extent the models' performances can represent human behavior. The results show that the transformers' performance is very similar to human behavior in many …
Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia
Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia
Theses and Dissertations
An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth.