Description

Book Synopsis

Part I-Fundamentals of Foundation Models.-Chapter 1-Foundation Models and Generative AI.- Chapter 2-Neural Networks.- Chapter 3- Learning and Generalization of Vision Transformers.- Chapter 4-Formalizing In-Context Learning in Transformers.- Part II Advanced Topics in Foundation Model.- Chapter 5-Automated Visual Prompting.- Chapter 6-Prompting Large Language Models with Privacy.- Chapter 7- Memory-Efficient Fine-Tuning for Foundation Models.- Chapter 8 Large Language Models Meet Time Series.- Chapter 9-Large Language Models Meet Speech Recognition.- Chapter 10-Benchmarking Foundation Models using Synthetic Datasets.- Chapter 11-Machine Unlearning for Foundation Models.- Chapter 12-Part III Trust and Safety in Foundation Models.- Chapter 12-Trustworthiness Evaluation of Large Language Models.- Chapter 13-Attacks and Defenses on Aligned Large Language Models.- Chapter 14- Safety Risks in Fine-tuning Large Language Models.- Chapter15- Watermarks for Large Language Models.- Chapter 16- AI-Generated Text Detection.- Chapter 17- Backdoor Risks in Diffusion Models.- Chapter 18- Prompt Engineering for Safety Red-teaming: A Case Study on Text-to-Image Diffusion Models.

Introduction to Foundation Models

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    Order before 4pm today for delivery by Tue 16 Jun 2026.

    A Hardback by Pin-Yu Chen

    15 in stock


      View other formats and editions of Introduction to Foundation Models by Pin-Yu Chen

      Publisher: Springer
      Publication Date: 8/9/2025
      ISBN13: 9783031767692, 978-3031767692
      ISBN10: 3031767691
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis

      Part I-Fundamentals of Foundation Models.-Chapter 1-Foundation Models and Generative AI.- Chapter 2-Neural Networks.- Chapter 3- Learning and Generalization of Vision Transformers.- Chapter 4-Formalizing In-Context Learning in Transformers.- Part II Advanced Topics in Foundation Model.- Chapter 5-Automated Visual Prompting.- Chapter 6-Prompting Large Language Models with Privacy.- Chapter 7- Memory-Efficient Fine-Tuning for Foundation Models.- Chapter 8 Large Language Models Meet Time Series.- Chapter 9-Large Language Models Meet Speech Recognition.- Chapter 10-Benchmarking Foundation Models using Synthetic Datasets.- Chapter 11-Machine Unlearning for Foundation Models.- Chapter 12-Part III Trust and Safety in Foundation Models.- Chapter 12-Trustworthiness Evaluation of Large Language Models.- Chapter 13-Attacks and Defenses on Aligned Large Language Models.- Chapter 14- Safety Risks in Fine-tuning Large Language Models.- Chapter15- Watermarks for Large Language Models.- Chapter 16- AI-Generated Text Detection.- Chapter 17- Backdoor Risks in Diffusion Models.- Chapter 18- Prompt Engineering for Safety Red-teaming: A Case Study on Text-to-Image Diffusion Models.

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