Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 80 for “"Network Inference"”.
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Network Inference Using Independence Criteria
… It is possible to derive regulatory models using network inference algorithms from high-throughput data, for example; from gene or protein expression data. A wide variety of network inference algorithms have been designed and implemented. Our aim is to explore the possibilities of using …
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Bayesian Network Inference Using Marginal Trees
Bayesian networks (BNs) are formal probabilistic graphical models for reasoning un- der uncertainty. BNs are used in a variety of applications, including the state-of- the-art forensic software tool, a ranking system for games, and landing the Mars Exploration Rover. A problem domain is modeled …
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Efficient convolutional neural network inference on microcontrollers
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
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Network inference via clustered fused graphical lasso
Embargo set by: Seth Robbins for item 107307 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system
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Synaptic Multimodal Imaging and Molecular Network Inference
… tightly regulated, and complex network of interactions between synaptic activity and hundreds of proteins and the mechanisms that regulate them. Isolating individual processes loses the context in which they occur, while bulk analyses average over highly heterogeneous populations …
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Towards causality in gene regulatory network inference
… small sample size settings for gene regulatory network inference. We then describe the use of single-cell genetic perturbation screens for determining the causal roles of critical regulatory complexes, focusing specifically on its applications for revealing mechanistic insights about the …
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Unsupervised gene regulatory network inference on microarray data
Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer gene networks for gene expression data, which are usually obtained from microarray experiments. A gene network is a method to …
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HASICs: Investigating hyperspecialized ASICs for neural network inference
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Large-Scale Optical Hardware for Neural Network Inference Acceleration
Artificial deep neural networks (DNNs) have revolutionized tasks such as automated classification and natural language processing. To boost accuracy and handle more complex workloads, DNN model sizes have grown exponentially over the last decade, outpacing improvements in digital electronic …
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Accelerating graph attention network inference on CPUs with layer fusion
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Improving the precision of toad network inference from GPS trajectories
Current approaches to construct road network maps from GPS trajectories suffer from low precision, especially in dense urban areas and in regions with complex topologies such as overpasses and underpasses, parallel roads, and stacked roads. This work shows how to improve precision without …
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Early-Stage Design Space Exploration Tool for Neural Network Inference Accelerators
Permanent URL: https://doi.org/10.7936/K7862FV4
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Advanced Network Inference Techniques Based on Network Protocol Stack Information Leaks
… explicit channels. This thesis focuses on network side channels, where information flow occurs in the TCP/IP network stack implementations of operating systems. I will describe three new types of idle scans: a SYN backlog idle scan, a RST rate-limit idle scan, and a hybrid idle scan. Idle …
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Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
… that relate to creating hard synthetic Bayesian networks for empirical research on inference algorithms. One method translates deceptive problems studied in genetic algorithms to a Bayesian network setting, showing that Bayesian networks can be deceptive. The other result is based on translating …
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Algorithms for regulatory network inference and experiment planning in systems biology
… mathematical model of the protein regulatory network controlling cell division in budding yeast. (ii) I formulate several natural problems related to efficient synthesis of a target mutant from source mutants. These formulations capture experimentally-useful notions of verifiability (e.g., the …
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Toward Predictable and Efficient Deep Neural Network Inference on Graphics Processing Units
GPUs dominate DNN inference but remain difficult to control predictably under multi-tenant load. This thesis presents a practical, end-to-end approach for predictable, efficient single-GPU inference built around a closed loop of predict → allocate → power-tune. First, we introduce SGPRS, a …
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Statistical Analysis of Gene Expression Profile: Transcription Network Inference and Sample Classification
… classifying samples, to discover regulatory gene networks using natural genetic perturbations, to develop statistical methods for model fitting and comparison of biochemical networks, and eventually to advance our capability to understand the principles of biological processes at the system level.
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Module-based Analysis of Biological Data for Network Inference and Biomarker Discovery
… aim to obtain global insight into the cellular networks. Several studies have unveiled the modular and hierarchical organization inherent in these networks. In this dissertation, we propose and develop innovative systems approaches to integrate multi-source biological data in a modular manner …
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