Sökning: "djupa neuronnät"
Visar resultat 1 - 5 av 20 uppsatser innehållade orden djupa neuronnät.
1. Pruning a Single-Shot Detector for Faster Inference : A Comparison of Two Pruning Approaches
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Modern state-of-the-art object detection models are based on convolutional neural networks and can be divided into single-shot detectors and two-stage detectors. Two-stage detectors exhibit impressive detection performance but their complex pipelines make them slow. LÄS MER
2. Probability of Default Term Structure Modeling : A Comparison Between Machine Learning and Markov Chains
Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : During the recent years, numerous so-called Buy Now, Pay Later companies have emerged. A type of financial institution offering short term consumer credit contracts. As these institutions have gained popularity, their undertaken credit risk has increased vastly. Simultaneously, the IFRS 9 regulatory requirements must be complied with. LÄS MER
3. Deep learning models as decision support in venture capital investments : Temporal representations in employee growth forecasting of startup companies
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Venture capital investors are constantly exposed to high levels of risk in investment scenarios. To lower that risks, various decision support tools can be exploited, such as machine learning models aimed at predicting startup success. LÄS MER
4. Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. LÄS MER
5. Uncertainty Estimation in Deep Neural Object Detectors for Autonomous Driving
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : One of the most challenging problems in autonomous driving, and in computer vision in general, is the task of object detection. Recently, the advances of deep learning in the domain of computer vision have led the research community to apply ideas from deep learning to the problem of object detection. LÄS MER
