ABSTRACT. Deep Neural Networks (DNNs), as the cornerstone of deep learning, have demonstrated their great success in diverse real-world applications, such as image classification, natural language processing, speech recognition, to name a few. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labor-intensive because of the trial-and-error process, and not easy to realize due to the rare expertise in practice.

Neural Architecture Search (NAS) is a kind of technique that could automatically designing promising DNN architectures by formulating the design process as optimization problems. Among existing optimizers for solving NAS, the Evolutionary Computation (EC) methods have demonstrated their powerful ability and have drawn increasing attention.

This tutorial will provide a comprehensive introduction to NAS techniques based on EC, i.e., evolutionary neural architecture search (ENAS), for automatically designing the architectures of DNNs. Specifically, this tutorial will cover the ENAS algorithms over 200 papers of most recent ENAS methods in light of the core components, to systematically show their design principles as well as justifications on the design. From this tutorial, the audiences are expected to get familiar with ENAS in four aspects.

ABSTRACT. The era of large-scale foundation models (e.g., LLMs, VLMs) presents a critical challenge: adapting them via traditional fine-tuning to new tasks becomes prohibitively expensive, which creates barriers for researchers and practitioners with limited resources. This tutorial introduces Neural Network Reprogrammability, a new perspective and paradigm for reusing pre-trained models without modifying model parameters and thus costly retraining. With this concept, we demonstrate how seemingly disparate parameter-efficient fine-tuning techniques, namely model reprogramming (MR), prompt tuning (PT), and in-context learning (ICL)—previously studied in isolation—share fundamental principles that can be unified under a coherent framework.

The tutorial provides both theoretical foundations and practical insights, illustrating how to repurpose pre-trained models for new tasks by harnessing reprogrammability, while reducing computational costs by orders of magnitude compared to traditional fine-tuning. Attendees will learn to leverage the inherent input sensitivity of neural networks for constructive model adaptation, master concrete techniques for aligning pre-trained models' outputs to new tasks, and explore applications beyond visual recognition and text generation to diverse domains like healthcare and time-series analysis.

ABSTRACT. Exchange of trusted information/knowledge among people plays an essential role in every aspect of their lives: socially, economically, and politically. Also, networks in the past 70 years, by making digital information ubiquitous, transformed our lives in unimaginable ways. This trend with new innovative technologies such Blockchain, Web3, Semantic Web and AI surely will continue at an accelerating pace in the years ahead. The tutorial covers Blockchain, Sematic Web and Decentralized AI, which is a synergistic combination for innovative applications.

Blockchain is a foundational innovation for keeping temper proof (trusted) data in a permanent, immutable, decentralized, global, and trustless ledger. It is a new field that combines distributed computing, databases, networks, cryptography, and economics, and is also rapidly evolving. It allows people, organizations, and machines to digitize their current relationships as well as forming new secure digital ones since data is securely recorded and shared in a blockchain database system. Moreover, new advances in WEB3.0 is taking place where individuals, organizations and machines are being empowered in a superior system of digital identity and trust in new services and products in many domains.

Semantic Web enables the explicit representation of knowledge in ontologies and deducing implicitly available knowledge by the machines, thus paving the way for the machines to process the knowledge and make decisions.