Transfer Learning through Embedding Spaces
Rostami, Mohammad
Sold by Books Puddle, New York, NY, U.S.A.
AbeBooks Seller since November 22, 2018
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: 4 available
Add to basketSold by Books Puddle, New York, NY, U.S.A.
AbeBooks Seller since November 22, 2018
Condition: New
Quantity: 4 available
Add to basket1st edition NO-PA16APR2015-KAP.
Seller Inventory # 26398985466
Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated.
This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently.
In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.
Mohammad Rostami is a computer scientist at USC Information Sciences Institute. He is a graduate of the University of Pennsylvania, University of Waterloo, and Sharif University of Technology. His research area includes continual machine learning and learning in data scarce regimes.
"About this title" may belong to another edition of this title.
We accept return for those books which are received damaged. Though we take appropriate care in packing to avoid such situation.
| Order quantity | 12 to 19 business days | 12 to 14 business days |
|---|---|---|
| First item | US$ 3.99 | US$ 6.99 |
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.