Sökning: "stochastic gradient descent"
Visar resultat 1 - 5 av 14 avhandlingar innehållade orden stochastic gradient descent.
1. Adaptiveness and Lock-free Synchronization in Parallel Stochastic Gradient Descent
Sammanfattning : The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative optimization procedure Stochastic gradient descent (SGD), is the backbone. LÄS MER
2. Selected Topics in Mathematical Modelling: Machine Learning and Tugs-of-War
Sammanfattning : This thesis concerns selected topics in mathematical modelling, namely in machine learning and stochastic games called tugs-of-war. It consists of four scientific articles. The first and second are about machine learning topics, while the third and fourth articles are about tug-of-war games. LÄS MER
3. Adaptiveness, Asynchrony, and Resource Efficiency in Parallel Stochastic Gradient Descent
Sammanfattning : Accelerated digitalization and sensor deployment in society in recent years poses critical challenges for associated data processing and analysis infrastructure to scale, and the field of big data, targeting methods for storing, processing, and revealing patterns in huge data sets, has surged. Artificial Intelligence (AI) models are used diligently in standard Big Data pipelines due to their tremendous success across various data analysis tasks, however exponential growth in Volume, Variety and Velocity of Big Data (known as its three V’s) in recent years require associated complexity in the AI models that analyze it, as well as the Machine Learning (ML) processes required to train them. LÄS MER
4. Convergence and stability analysis of stochastic optimization algorithms
Sammanfattning : This thesis is concerned with stochastic optimization methods. The pioneering work in the field is the article “A stochastic approximation algorithm” by Robbins and Monro [1], in which they proposed the stochastic gradient descent; a stochastic version of the classical gradient descent algorithm. LÄS MER
5. Distributed Stochastic Programming with Applications to Large-Scale Hydropower Operations
Sammanfattning : Stochastic programming is a subfield of mathematical programming concerned with optimization problems subjected to uncertainty. Many engineering problems with random elements can be accurately modeled as a stochastic program. In particular, decision problems associated with hydropower operations motivate the application of stochastic programming. LÄS MER
