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Articles 1 - 8 of 8
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Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Graduate Theses and Dissertations
With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Graduate Theses and Dissertations
The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Graduate Theses and Dissertations
This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …
Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha
Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha
Graduate Theses and Dissertations
Super-resolution has emerged as a crucial research topic in the field of Magnetic Resonance Imaging (MRI) where it plays an important role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. Deep learning techniques have been successful in achieving state-of-the-art results for super-resolution. These deep learning-based methods heavily rely on a substantial amount of data. Additionally, they require a pair of low-resolution and high-resolution images for supervised training which is often unavailable. Particularly in MRI super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To overcome this, existing methods for …
Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey
Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey
Computer Science and Computer Engineering Undergraduate Honors Theses
The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of …
Chicken Keypoint Estimation, Rohit Kala
Chicken Keypoint Estimation, Rohit Kala
Computer Science and Computer Engineering Undergraduate Honors Theses
Poultry is an important food source across the world. To facilitate the growth of the global population, we must also improve methods to oversee poultry with new and emerging technologies to improve the efficiency of poultry farms as well as the welfare of the birds. The technology we explore is Deep Learning methods and Computer Vision to help automate chicken monitoring using technologies such as Mask R-CNN to detect the posture of the chicken from an RGB camera. We use Meta Research's Detectron 2 to implement the Mask R-CNN model to train on our dataset created on videos of chickens …
Characterization Of 2d Quantum Materials Using Ai And Large-Scale Quantum Data Collection, Apoorva Bisht
Characterization Of 2d Quantum Materials Using Ai And Large-Scale Quantum Data Collection, Apoorva Bisht
Computer Science and Computer Engineering Undergraduate Honors Theses
2D materials like hexagonal boron nitride, graphene, and tungsten diselenide are widely utilized for studying their unique mechanical and opto-electronic properties to exploit them to make transistors and fabricating a variety of other devices. All these applications require that the 2D materials used be of specific uniform thickness. Until very recently, this process has been largely manual and tedious. However, few applications exploit the characteristic color-to-thickness correspondence of these near-transparent materials. To continue this effort, in this work we create a large-scale dataset for three different materials (hBN, graphene, and WSe$_2$) to train and test an image segmentation model along …
Achieving Causal Fairness In Recommendation, Wen Huang
Achieving Causal Fairness In Recommendation, Wen Huang
Graduate Theses and Dissertations
Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …