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 642 for “"Learning Model"”.
-
Machine Learning Model Watermarking through DRAM PUFs
… rising cost of training, security concerns over model theft have also emerged, where an adversarial party may replicate a pre-trained model without proper authorization and deploy it for their advantage. Watermarking serves as a tool that, in such scenarios, allows the legitimate owner to claim …
-
Unsupervised Learning : Model-guided and Model-agnostic Approaches
Unsupervised learning is the branch of machine learning that is aimed at learning patterns from data without labels. Supervised learning with millions of labels for image classification had driven the modern deep learning revolution in the past few years. Deep neural networks have exceeded human …
-
Efficient Deep Learning: Model Design and Algorithmic Innovation
… of Artificial Intelligence (AI) and Deep Learning (DL) has revolutionized numerous domains, from computer vision to natural language processing and intelligent recommendation systems. However, this progress has been accompanied by escalating computational demands that challenge the …
-
Stakeholder-based sustainable design: a participatory learning model
… In this thesis, I examine the efficacy of a Learning Circle, a participatory learning model, in building the competence and confidence of stakeholders in Mattoon, Illinois to engage in a sustainable design process for their communities, and in facilitating buy-in within the community.
-
A Machine Learning Model for Octane Number Prediction
… al. 2018). Previous research has used empirical models in the form of phenomeno-logical and machine learning models (Gonz´alez 2019). Phenomeno-logical models have been used in the past as a way of programming an engineer's thought process in the form of differential equations put together. …
-
Probing, Improving, and Verifying Machine Learning Model Robustness
Machine learning models turn out to be brittle when faced with distribution shifts, making them hard to rely on in real-world deployment. This motivates developing methods that enable us to detect and alleviate such model brittleness, as well as to verify that our models indeed meet desired …
-
Collaborative Community based Self-Expanding (CCSE) E-Learning Model
… Community-Based Self-Expanding (CCSE) eLearning Model to support the development of a collaborative, expansive, and community-driven online learning environment. A thorough literature review on modern online learning, collaborative eLearning, self-expanding approaches, and community …
-
Educating Incarcerated Youth In Illinois: A Blended Learning Model
… the apparent continued success of a blended learning educational model in place since 2012 in the Illinois Department of Juvenile Justice (IDJJ). Using a mixed methods approach, data were gathered and analyzed from a variety of records, reports, and other documentation that included: diplomas …
-
Software-Hardware Co-design For Deep Learning Model Acceleration
<p>Current deep neural network (DNN) models have shown beyond-human performance in multiple artificial intelligent tasks. However, state-of-the-art DNN models still exhibit great issues on efficiency that pose significant obstacles to their practical application in real-world scenarios. To further …
-
A Learning ‘Learning’ Model for Optimised Construction Workforce Development
Integrating learning and work has become important for several reasons. The recognition that the key resources for wealth creation, knowledge and ideas are embedded in human capital. Furthermore, fast-paced advances in knowledge, technology, and access to information ensure that capabilities …
-
A machine learning model for vehicle crash type prediction
… of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify …
-
Recommending TEE-based Functions Using a Deep Learning Model
… ML-TEE, a recommendation tool that uses a deep learning model to classify whether an input function handles sensitive information or sensitive code. By applying ML-TEE, developers can reduce the burden of manual code inspection and analysis. ML-TEE's model was trained and tested on functions …
-
3D Hand Pose Estimation Via a Lightweight Deep Learning Model
Deep Learning with depth cameras has enabled 3D hand pose estimation from RGBD images. Commercial solutions like Leap Motion and Intel RealSense™ use stereoscopic sensors or IR illumination-based methods to capture the depth in a photograph and further estimate pose using Deep Learning (DL) …
-
One-Shot Learning Model for Cancer Diagnosis from Histopathological Images
… into the nature of problem and propose a single model which can diagnose several types of cancers. Further, we employ recent advances in one-shot learning to enable our model to learn and expand to different types of cancer only from a few examples. We demonstrate good performance of our model on …
-
Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation
<p>Machine learning model performance on both validation data and new data can be better measured and understood by leveraging uncertainty metrics at the time of prediction. These metrics can improve the model training process by indicating which training data need to be corrected and what part of …
-
Profiling and characterization of deep learning model inference on CPU
With the rapid growth of deep learning models and higher expectations for their accuracy and throughput in real-world applications, the demand for profiling and characterizing model inference on different hardware/software stacks is significantly increased. As the model inference characterization on …
-
ModelDB : tools for machine learning model management and prediction storage
Building a machine learning model is often an iterative process. Data scientists train hundreds of models before finding a model that meets acceptable criteria. But tracking these models and remembering the insights obtained from them is an arduous task. In this thesis, we present two main systems …
-
Enhancing the Verification-Driven Learning Model for Data Structures with Visualization
… structures algorithms using the Visualization Learning tool. The main objective of the work is to provide a learning opportunity for novice computer science students to gain a broader exposure towards data structure programming. The visualization learning tool is based on the …
-
Looping predictive method to improve accuracy of a machine learning model
… tweets. The goal is to build a Machine Learning Model that can distinguish between tweets that indicate drug abuse and other tweets that also contain the name of a drug but do not describe abuse. Drugs can be illegal, such as heroin, or legal drugs with a potential of abuse, such as …
-
Demystifying a dark art: Understanding real-world machine learning model development
… that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. Users currently rely on empirical trial-and-error to obtain their own set of battle-tested guidelines to inform their modeling …
Page 1 of 33