Bøker av Xiaoli Li

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.

3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.