Essays about: "Model Based Inference"

Showing result 1 - 5 of 118 essays containing the words Model Based Inference.

  1. 1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Xinchen Wang; [2024]
    Keywords : Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

    Abstract : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. READ MORE

  2. 2. From Tree Huggers to Money Makers: How ESG Scores Boost Corporate Financial Performance in the EU

    University essay from

    Author : Martin Ekendahl; Philippa Hedström; [2023-06-29]
    Keywords : ESG score; financial performance; stakeholder theory; the EU; fixed effects model;

    Abstract : This thesis examines the relationship between Environmental, Social, and Governance (ESG) scores and financial performance as measured by both accounting- and market-based measures; Return on Assets and Tobin's Q. The study takes a particular focus on the European Union (EU), more specifically on companies operating in the region and the union's strong commitment to achieving the Sustainable Development Goals (SDGs) and more general efforts towards a more sustainable future. READ MORE

  3. 3. Autoencoder-Based Likelihood-Free Parameter Inference of Gene Regulatory Network

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Liang Cheng; [2023]
    Keywords : ;

    Abstract : Likelihood-free parameter inference is a well-known statistical methodology that estimates the posterior distribution of model parameters even in cases where the likelihood function is intractable. The performance of this method is highly correlated with the learning of summary statistics, which capture the key features from the high dimensional data such as time series. READ MORE

  4. 4. Estimating eco-friendly driving behavior in various traffic situations, using machine learning

    University essay from Linköpings universitet/Institutionen för datavetenskap

    Author : Ludvig Fors; [2023]
    Keywords : Machine learning; transformers; neural networks; casual inference; K-Means; driver behavior; fuel consumption;

    Abstract : This thesis investigates how various driver signals, signals that a truck driver can interact with, influences fuel consumption and what are the optimal values of these signals in various traffic conditions. More specifically, the objective is to estimate good driver behavior in various traffic conditions and compare bad driver behavior in similar situations to see how performing a specific driver action, changing a driver signal from the bad driver value to the corresponding good driver value impacts the fuel consumption. READ MORE

  5. 5. Robust Non-Linear State Estimation for Underwater Acoustic Localization : Expanding on Gaussian Mixture Methods

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Diogo Antunes; [2023]
    Keywords : Robust state estimation; Underwater localization; Target tracking; Gaussian mixture; AUV; Estimação robusta de estado; Localização subaquática; Rastreamento de alvos; Mistura Gaussiana; AUV; Robust tillståndsuppskattning; Undervattenslokalisering; Målspårning; Gaussisk blandning; AUV;

    Abstract : Robust state estimation solutions must deal with faulty measurements, called outliers, and unknown data associations, which lead to multiple feasible hypotheses. Take, for instance, the scenario of tracking two indistinguishable targets based on position measurements, where each measurement could refer to either of the targets or even be a faulty reading. READ MORE