Towards Human Brain Inspired Lifelong Learning
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B01=Arulmurugan Ambikapathi
B01=Haytham M Fayek
B01=Savitha Ramasamy
B01=Suresh Sundaram
B01=Xiaoli Li
Bayesian Modelling
Category1=Non-Fiction
Category=UYQM
Continual Learning
COP=Singapore
Data Analysis
Deep Learning
Delivery_Delivery within 10-20 working days
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eq_nobargain
eq_non-fiction
Image Classification
Image Processing
Language_English
Meta-learning
Non-stationary Data
PA=Available
Price_€50 to €100
PS=Active
softlaunch
Product details
- ISBN 9789811286704
- Publication Date: 02 May 2024
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
- Language: English
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Over the past few decades, the field of machine learning has made remarkable strides, surpassing human performance in tasks like voice and object recognition, as well as mastering various complex games. Despite these accomplishments, a critical challenge remains: the absence of general intelligence. Achieving artificial general intelligence (AGI) requires the development of learning agents that can continually adapt and learn throughout their existence, a concept known as lifelong learning.In contrast to machines, humans possess an extraordinary capacity for continuous learning throughout their lives. Drawing inspiration from human learning, there is immense potential to enable artificial learning agents to learn and adapt continuously. Recent advancements in continual learning research have opened up new avenues to pursue this objective.This book is a comprehensive compilation of diverse methods for continual learning, crafted by leading researchers in the field, along with their practical applications. These methods encompass various approaches, such as adapting existing paradigms like zero-shot learning and Bayesian learning, leveraging the flexibility of network architectures, and employing replay mechanisms to enable learning from streaming data without catastrophic forgetting of previously acquired knowledge.This book is tailored for researchers, practitioners, and PhD scholars working in the realm of Artificial Intelligence (AI). It particularly targets those envisioning the implementation of AI solutions in dynamic environments where data continually shifts, leading to challenges in maintaining model performance for streaming data.
