31 resultater (0,32301 sekunder)

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Imaging Complications of Gastrointestinal and Biliopancreatic Endoscopy Procedures - - Bog - Springer International Publishing AG - Plusbog.dk

Contemporary Management of Jugular Paraganglioma - - Bog - Springer International Publishing AG - Plusbog.dk

Clinical Investigations in Gastroenterology - Malcolm C. Bateson - Bog - Springer International Publishing AG - Plusbog.dk

Clinical Investigations in Gastroenterology - Malcolm C. Bateson - Bog - Springer International Publishing AG - Plusbog.dk

Coherent States - Da Hsuan Feng - Bog - Springer International Publishing AG - Plusbog.dk

Artificial Neural Networks - Ivan Nunes Da Silva - Bog - Springer International Publishing AG - Plusbog.dk

Visual Domain Adaptation in the Deep Learning Era - Mathieu Salzmann - Bog - Springer International Publishing AG - Plusbog.dk

Visual Domain Adaptation in the Deep Learning Era - Mathieu Salzmann - Bog - Springer International Publishing AG - Plusbog.dk

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.

DKK 425.00
1

Administrative Law for the 21st Century - Suzana Tavares Da Silva - Bog - Springer International Publishing AG - Plusbog.dk

Elite Techniques in Shoulder Arthroscopy - - Bog - Springer International Publishing AG - Plusbog.dk

Materializing the Foundations of Quantum Mechanics - Climerio Paulo Da Silva Neto - Bog - Springer International Publishing AG - Plusbog.dk

Transfer Learning for Multiagent Reinforcement Learning Systems - Felipe Leno Da Silva - Bog - Springer International Publishing AG - Plusbog.dk

Transfer Learning for Multiagent Reinforcement Learning Systems - Felipe Leno Da Silva - Bog - Springer International Publishing AG - Plusbog.dk

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.

DKK 519.00
1

Decoding Astronomy in Maya Art and Architecture - Marion Dolan - Bog - Springer International Publishing AG - Plusbog.dk

Friendship 7 - Colin Burgess - Bog - Springer International Publishing AG - Plusbog.dk

Friendship 7 - Colin Burgess - Bog - Springer International Publishing AG - Plusbog.dk

In this spellbinding account of an historic but troubled orbital mission, noted space historian Colin Burgess takes us back to an electrifying time in American history, when intrepid pioneers were launched atop notoriously unreliable rockets at the very dawn of human space exploration. A nation proudly and collectively came to a standstill on the day this mission flew; a day that will be forever enshrined in American spaceflight history. On the morning of February 20, 1962, following months of frustrating delays, a Marine Corps war hero and test pilot named John Glenn finally blazed a path into orbit aboard a compact capsule named Friendship 7. The book’s tension-filled narrative faithfully unfolds through contemporary reports and the personal recollections of astronaut John Glenn, along with those closest to the Friendship 7 story, revealing previously unknown facts behind one of America’s most ambitious and memorable pioneering space missions. Friendship 7. The book’s tension-filled narrative faithfully unfolds through contemporary reports and the personal recollections of astronaut John Glenn, along with those closest to the Friendship 7 story, revealing previously unknown facts behind one of America’s most ambitious and memorable pioneering space missions. Friendship 7 story, revealing previously unknown facts behind one of America’s most ambitious and memorable pioneering space missions.

DKK 319.00
1

The Space Launch System - Anthony Young - Bog - Springer International Publishing AG - Plusbog.dk

Integrated Uncertainty in Knowledge Modelling and Decision Making - - Bog - Springer International Publishing AG - Plusbog.dk

The Vacuum Cleaner - Maud Ellmann - Bog - Springer International Publishing AG - Plusbog.dk