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Showing 1 to 20 of 231 for “"Machine Learning Model"”.
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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 …
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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. …
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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 …
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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 …
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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 …
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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 …
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Looping predictive method to improve accuracy of a machine learning model
… of drug-related 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 …
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Demystifying a dark art: Understanding real-world machine learning model development
It is well-known 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 …
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The synthetic student : a machine learning model to simulate MOOC data
… Massive Open Online Courses (MOOCs) offer a learning opportunity to anyone with a computer - as well as an opportunity for researchers to investigate student learning through the accumulation of data about student-course interactions. Unfortunately, efforts to mine student data for …
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A machine learning model of Manhattan air pollution at high spatial resolution
A machine-learning model was created to predict air pollution at high spatial resolution in Manhattan, New York using taxi trip data. Urban air pollution increases morbidity and mortality through respiratory and cardiovascular impacts, and understanding and predicting it is a significant public …
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Influence of training dataset selection on the performance of a machine learning model
… located at the University of Saskatchewan. The model has been developed using Deep Learning (DL) based Multi-column Convolutional Neural Network (MCNN) algorithm and TensorFlow framework. This is an object counting model, that counts the Canola flowers from the images based on the learning from …
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A Machine Learning Model for Understanding How Users Value Designs: Applications for Designers and Consumers
… a number of advances toward developing a machine learning (ML) model of how designs are valued by their users. The model can be used to better understand the implications of furniture design decisions, as well as for commercial strategy. Existing ML systems have been trained on the …
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The Motivational Effects of Feedback: Development of a Machine Learning Model to Predict Student Motivation from Professor Feedback
… on the recipient's motivation. A transformer machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Feedback was defined and evaluated from the perspective of …
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Unravelling black box machine learning methods using biplots
… industry is moving toward the use of new machine learning methods, such as neural networks, and away from older methods such as generalised linear models. However, their use is currently limited because they are seen as “black box” models, which gives predictions without justifications and …
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Integrating Machine Learning into Data Analysis and Plant Performance
… group of Nissan manufacturing plants, that machine learning can be applied to plant performance data to identify and prioritize metrics and to better understand the impact of those metrics on overall plant performance. Nissan already benchmarks plant performance between its manufacturing …
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Solving Machine Learning Problems
Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course exercises, homework, and quiz questions from MIT’s 6.036 …
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