Overview
Neural Architecture Search (NAS) has emerged as a revolutionary approach to automating the intricate design of Artificial Neural Networks (ANNs). Over the past decade, NAS has captivated the attention and enthusiasm of both the Computational Intelligence and Machine Learning communities.

Traditionally, NAS algorithms are grouped into three main categories based on their search methodologies:

1. 1. Reinforcement Learning
   2. Gradient-based Techniques
   3. Evolutionary Computation

Yet, the landscape of NAS is continually evolving. Modern NAS innovations often defy these clear-cut classifications. Some algorithms exhibit characteristics spanning multiple categories, while others introduce groundbreaking architectural encodings that challenge conventional search methods.

The dynamism of this field is undeniable. Contemporary NAS techniques are pushing boundaries by enabling the design of complex Deep Neural Networks within stringent time constraints. This progress is driven by:

• • Streamlined Search Spaces: Efficiently defined to focus on optimal solutions.
  • GPU Optimisation: Leveraging the power of GPUs for accelerated computations.
  • Approximation Techniques: Using intelligent approximating functions to enhance performance.

Notably, while some NAS approaches excel in specific domains, others produce adaptable architectures, empowering users to effortlessly transition between diverse applications.

This workshop aims to delve into the contemporary trends of NAS, explore its successes and challenges, and contemplate its future directions and broader impact within the AI community, industry, and society.

Date: To be advised
Time: To be advised
Venue: To be advised

Organisers

Overview
Computational Intelligence and Machine Learning have emerged as the driving forces behind advancements in the field of information technology. Rapid progress in related domains not only offers fresh perspectives for scientific inquiry but also exerts profound impacts across a myriad of practical applications. This workshop “Advances in Computational Intelligence and Machine Learning: Applications and Innovations” aims to explore the latest developments in Computational Intelligence and Machine Learning, showcasing their practical applications and innovations across various domains.
This workshop will encompass the following aspects:

• • Theory and Methods: We will delve into the latest theories and methods within the realms of Computational Intelligence and Machine Learning. This includes, but is not limited to, Deep Learning, Reinforcement Learning, Evolutionary Algorithms, Fuzzy Logic, Genetic Algorithms, and more. Discussions will revolve around the principles, advantages, and applications of these methodologies across diverse problem domains.
  • Practical Applications: Emphasis will be placed on the application of Computational Intelligence and Machine Learning in real-world scenarios. This encompasses Natural Language Processing, Computer Vision, Medical Diagnostics, and Financial Forecasting, among other fields. We will explore how these applications are reshaping our lives and work processes, as well as discuss their potential impacts and challenges.
  • Innovation and Future Trends: Discussions will center around the innovations and future trends in Computational Intelligence and Machine Learning. This includes emerging algorithms, novel application domains, and new research directions. We will explore how these innovations drive technological advancements and societal developments, while also deliberating on potential future directions and challenges.

Through this workshop, we aim to provide a platform for participants to exchange ideas and learn from one another, fostering collaboration and innovation within the realms of Computational Intelligence and Machine Learning. By facilitating dialogue and knowledge-sharing, we seek to propel advancements in these fields, addressing the increasingly complex and diverse technological and societal challenges of our time.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: ACIML

Organisers
Ong Yew Soon (School of Computer Science and Engineering, Nanyang Technological University, Singapore)
Huanhuan Chen (School of Computer Science and Technology, University of Science and Technology of China, China)
Shan He (School of Computer Science, University of Birmingham, UK)
Bing Xue (School of Engineering and Computer Science, Victoria University of Wellington, New Zealand)

Overview
Artificial Intelligence (AI) is revolutionising the transportation sector including maritime, land, and aerospace industries, introducing unprecedented advancements for the next stage of transport connectivity and further economic growth. This workshop delves into the innovative applications of AI focusing on maritime operations, port traffic management, urban mobility, electric and autonomous transportation systems including aerospace. Emphasising the intersection of AI, machine learning models, and predictive analytics, the symposium highlights their potential to revolutionise transportation by reducing emissions and enhancing safety. Together we will discover how AI-driven insights can refine vehicle and vessel operations, paving the way for sustainable and safer transportation solutions. Don’t miss the opportunity to be part of the next stage of transport connectivity and economic growth.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: AI-DIIT

Organisers
Xiuju FU (Institute of High Performance Computing, A*STAR, Singapore)
Ran YAN (Nanyang Technological University, Singapore)
Zhiyong CUI (Beihang University, China)
Shuo FENG (Tsinghua University, China)
Yonggang LUO (AI Lab, Chongqing Changan Automobile, China)
Abhinav SAXENA (GE Aerospace Research, USA)
Xiao Feng YIN (Institute of High Performance Computing, A*STAR, Singapore)
Yunhui LIN (Institute of High Performance Computing, A*STAR, Singapore)
Hongwei WANG (Institute of High Performance Computing, A*STAR, Singapore)
Ping Chong CHUA (Institute of High Performance Computing, A*STAR, Singapore)

Overview
Recent advanced AI technologies, especially the large language models (LLMs) like GPTs, have significantly advanced the field of natural language processing (NLP) and led to the development of various LLM-based applications. One potential application is as communication interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students and the machine capabilities including its own. This approach has several benefits, including the ability to personalise interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist in implementing this approach, including the need for more robust models, designing effective user interfaces, and ensuring ethical considerations are addressed.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: AI4EDU
Call for Paper: AI4EDU CFP

Organisers
Qingsong Wen (Squirrel AI, USA)
Joleen Liang (Squirrel AI, China)
Richard Tong (Squirrel AI, USA)
Yu Lu (Beijing Normal University, China)
Liu Guimei (A*STAR, Singapore)
Xiangen Hu (The University of Memphis, Tennessee, USA)
Robby Robson (Eduworks, USA)

Overview
Energy is the cornerstone of societal prosperity and its production, delivery, and management are essential to mankind. As the dynamic energy field continues to evolve, it’s crucial that we broaden our perspective to include control and optimisation within intricate energy systems. The escalating energy needs of developing economies are undeniable. However, considering our planet’s finite resources and the significant climate impact from the power sector, it’s essential to devise strategies that maintain environmental integrity and champion sustainability. Integrating AI into the energy business requires trust, explainability, and fairness. By making AI systems trustworthy, transparent, and fair, we can use AI to solve difficult energy production, distribution, and management problems while protecting the environment and promoting sustainability.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: AI For Enegry

Organisers
Zita Vale (Instituto Superior de Engenharia do Porto, Portugal)
G. Kumar Venaygamoorthy (Clemson University, USA)
João Soares (Instituto Superior de Engenharia do Porto, Portugal)

Overview
Pressing environmental challenges, such as climate change, deforestation, pollution, and loss of biodiversity, require urgent global and local attention. Addressing these issues demands multidisciplinary collaborative efforts to develop and implement large-scale sustainable solutions, ensuring the health and resilience of our planet. The machine learning (ML), or artificial intelligence (AI) community wishes to take action on solving these environmental issues but is often uncertain about the most effective intervention with maximum impacts. This workshop seeks to highlight potential environment-related research using ML/AI tools and illustrate the invaluable role ML/AI can play in reducing greenhouse gas emissions, large-scale estimation of carbon stock and biodiversity, smart and green transportation systems, effective optimisation of natural resources such as water and fisheries, ensuring sustainable agricultural practices, and resilience adaptation/mitigation practices to the reality of the changing climate. Many of these actions not only present opportunities for substantial real-world impact but also pose intriguing challenges for academic research.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: AI4EI
Call for Paper: AI4EI CFP

Organisers
Khoa D Doan (VinUniversity & CEI, Vietnam)
Huong T (Helen) Nguyen (University of Illinois at Urbana-Champaign, USA)
Nitesh Chawla (University of Notre Dame/Lucy Family Institute for Data and Society, USA)
Alexandre D’Aspremont (Ecole Normale Supérieure, France)
Karina Gin Yew-Hoong (National University of Singapore, Singapore)
Erick G. Sperandio Nascimento (Surrey Institute for People-Centred AI, University of Surrey, UK)
Harry Nguyen (University College Cork, Ireland)
Kok-seng Wong (VinUniversity & CEI, Vietnam)
Doanh N Nguyen (VinUniversity & CEI, Vietnam)
Alex A. Bandeira Santos (SENAI CIMATEC, Brazil)
Arshdeep Singh (Centre for Vision, Speech and Signal Processing, University of Surrey, UK)

Overview
Blockchain and Artificial Intelligence (AI) have individually transformed industries, and their convergence is poised to be the next frontier of innovation. This workshop aims to explore the multifaceted relationship between blockchain and AI, offering a platform for researchers, practitioners, and enthusiasts to discuss the latest developments, challenges, and opportunities at the intersection of these cutting-edge technologies. This workshop will provide a comprehensive overview of the synergies between blockchain and AI, fostering collaboration and innovation in these exciting fields. Participants will have the opportunity to network, share insights, and contribute to the advancement of this intersection of technologies.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: BCAI 2024
Call for Paper: BCAI 2024 CFP

Organisers
Goh Siow Mong Rick (A*STAR, Singapore)
Liu Yong (A*STAR, Singapore)
Wei Qingsong (A*STAR, Singapore)
Toyoda Kentaroh (A*STAR, Singapore)
Liu Yang (Nanyang Technological University, Singapore)
David Lo (Singapore Management University, Singapore)
Wei Wang (Hong Kong University of Science and Technology, Hong Kong, China)
Jin Zhang (Southern University of Science and Technology, Shenzhen, China)
Xiaofan Liu (City University of Hong Kong, AI-Powered Financial Technologies Ltd, Hong Kong, China)

Overview
From autonomous vehicles and 3D printing through to smart cities and digital twins, the gap between our physical and digital worlds is growing ever smaller. At the interface of these two worlds is Data-Centric Engineering, a rapidly emerging new branch of science for the 21st century which combines the power and insight available from large-scale data sources with the tools and technology that shape our real-world environment. Bridging the gap between the digital and physical realms requires the development of new machine learning algorithms that are capable of processing large volumes of data from satellites, cell phones, distributed sensors, etc. and making robust and transparent decisions that improve our daily lives and protect our natural environment. As Data-Centric Engineering is an emerging discipline it needs to define its boundaries, determine the techniques of importance, and nurture a diverse community of ECRs to take these ideas forward. The focus of this workshop is to bring the engineering and machine learning communities together to define Data-Centric Engineering, through keynote talks and contributed sessions, panel discussions, poster presentations and networking.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: Crafting Data-Centric Engineering

Organisers
Adam Sobey (The Alan Turing Institute, UK)
Gabin Kayumbi (The Alan Turing Institute, UK)
Jiangyan Shao (Wuhan University of Technology, China)
William Cooper (The Alan Turing Institute, UK)
Zack Xuereb Conti (The Alan Turing Institute, UK)
Lawrence Bull (University of Cambridge, UK)

Overview
In our rapidly changing world, the ability to learn and adapt is paramount. This capability enables us to navigate complex challenges, leveraging our past experiences to avoid previous pitfalls and apply knowledge in novel contexts. Remarkably, the human capacity for selecting and generalising relevant experiences to new problems stands as a testament to our cognitive sophistication.

Within the context of computational intelligence, several core learning technologies in neural and cognitive systems, fuzzy systems, probabilistic and possibilistic reasoning, have shown promise in emulating the generalisation capabilities of human learning, with many now used routinely to enhance our daily lives. Recently, in contrast to traditional machine learning approaches, Transfer Learning, which uses data from related source tasks to augment learning in a new (target) task, has attracted extensive attention and demonstrated great success in a wide range of real-world applications, including computer vision, natural language processing, speech recognition, etc.

In spite of several advances in computational intelligence, it is noted that the attempts to emulate such cognitive capabilities in problem solvers, especially those of an evolutionary nature, have received far less attention. In fact, most existing evolutionary algorithms (EAs) remain ill-equipped to exploit the potentially rich sources of knowledge that may be embedded in previous searches. With this, and the observation that any practically useful industrial system is likely to face a large number of (possibly repetitive) problems over a lifetime, it is contended that novel research advances in both the theory and application of Transfer Learning, especially for Optimisation, are primed to bring about a new wave in the real-world impact of intelligent systems.

The main goal of this workshop is to promote the research on crafting novel algorithm designs as well as theoretical analysis towards “intelligent” evolutionary computation, which possesses the transfer capabilities that evolve along with the problems solved. Further, this workshop also aims at providing a forum for academic and industrial researchers to explore future directions of research and promote evolutionary computation techniques to a wider audience in the society of computer science and engineering.

Date: To be advised
Time: To be advised
Venue: To be advised

Organisers

Overview
Foundation models have garnered significant attention from both research and industry communities. It holds profound potential for healthcare applications. These models, which can be trained using vast amounts of unlabeled data, including medical images, medical reports, and Electronic Medical Records (EMR), without requiring explicit data annotation, hold immense potential for healthcare AI. Their ability to be trained with diverse data from multiple sources offers numerous benefits, including reduced reliance on data annotation for fine-tuning and the capacity for robust generalisation in real-world applications across various cohorts, ethnicities, and devices. This workshop aims to explore and advance the application of foundation models, especially multimodal foundation model including medical image, medical reports, genetic, etc. It will also look into foundation models from the lens of robustness, privacy preserving, explainable, federated, and distributed perspectives in healthcare.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: FM4H

Organisers
Yong Liu (A*STAR, Singapore)
Goh Siow Mong Rick (A*STAR, Singapore)
Xinxing Xu (A*STAR, Singapore)
Dacheng Tao (The University of Sydney, Australia)
Dinggang Shen (ShanghaiTech University, China)
Li Shuo (Case Western Reserve University, USA)

Overview
Evolutionary computation is a family of algorithms modeled on natural evolution. It applies mechanisms like mutation, crossover, and selection to a population of candidate solutions, allowing them to evolve over generations. This approach excels in exploring vast search spaces and addressing complex optimisation tasks, making it a powerful tool in fields like engineering and artificial intelligence.

Traditionally, the category of evolutionary computation encompasses several sub-categories and techniques. Here is a list of some of the main categories within this field:

1. 1. Genetic Algorithms (GA)
   2. Evolution Strategies (ES)
   3. Genetic Programming (GP)
   4. Swarm Intelligence Algorithms
   5. Differential Evolution (DE)
   6. Ant Colony Optimisation (ACO)
   7. Artificial Immune Systems (AIS), etc.

The dynamism and advanced research fields of evolutionary computation are vast and diverse, reflecting the ever-evolving nature of this field. Here is a list of some of the current dynamic and advanced research areas within evolutionary computation:

• • Hybridisation and Integration with Other Methods
  • Multi-objective Optimisation
  • Large-scale Optimisation
  • Constrained Optimisation
  • Evolutionary Multitasking and Transfer Optimisation
  • Evolutionary Deep Learning
  • Evolutionary Theory
  • Real-world Applications, etc.

These are just a few examples of the dynamism and advanced research fields within evolutionary computation. As the field continues to evolve, new challenges and opportunities will arise, driving further advancements in these and other areas. This workshop aims to delve into the contemporary trends of evolutionary computation techniques, explore its successes and challenges, and contemplate its future directions and broader impact within the AI community, industry, and society.

Date: To be advised
Time: To be advised
Venue: To be advised

Organisers

Overview
The explosive success of large language models (LLMs) has captured the attention of the deep learning research community, and their powerful capabilities have demonstrated remarkable performance in various fields, such as natural language processing and computer vision. For example, since the ChatGPT of OpenAI has been online, it has accumulated over 10 billion users. However, LLMs face significant efficiency challenges, especially when deploying large neural networks with billions of parameters on edge devices such as personal computers and smartphones, which is impractical. In addition, LLMs have shown strong reliability issues (e.g., adversarial vulnerability, biased predictions, hallucinations, etc.), further impeding their applications in practice. These challenges have provided a strong impetus for model compression/robustness research in the era of LLMs, leading to the development of resource-efficient and robustness-enhancement methods. The research theme has brought challenges and opportunities to the deep learning community, requiring experts in deep learning, natural language processing, and computer vision to adopt interdisciplinary approaches to uncover the underlying mechanisms and develop practical solutions. By studying and addressing the efficiency and reliability challenges associated with LLMs, we could better enable practitioners to construct practical LLMs across various domains. The workshop will bring together researchers and practitioners from the machine learning communities to explore the latest advances and challenges in building practical learning methods, with a special focus on the efficiency and reliability of LLMs. The workshop consists of invited talks by leading experts in the field and contributed talks and poster sessions featuring the latest research.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: Practical-DL
Call for Paper: Practical-DL CFP

Organisers
Haotong Qin (Beihang University, China)
Ruihao Gong (SenseTime Research)
Jiakai Wang (Zhongguancyn Laboratory, China)
Qing Guo (A*STAR, Singapore)
Daquan Zhou (ByteDance, USA)
Olivera Kotevska (Oak Ridge National Laboratory, USA)
Aishan Liu (Beihang University, China)
Zhen Dong (UC Berkeley, USA)
Shanghang Zhang (Peking University, China)
Huanrui Yang (UC Berkeley, USA)
Xianglong Liu (Beihang University, China)
Dacheng Tao (Nanyang Technological University, Singapore)

Overview
In recent years, applications of machine learning for science and technology have been growing under the name Scientific Machine Learning (SciML). Techniques of this field are expected to accelerate essential processes in industry, such as physical simulations and the discovery of new drugs. Although more and more researchers and industrial companies are paying attention to this research area, there are still few opportunities to communicate with each other. This workshop aims to introduce the latest results in this field and to provide opportunities to meet, interact and start collaboration. This workshop also aims to present the latest research in scientific machine learning to clarify the benefits of the techniques in this field to the audience, provide a platform for the research groups in this area to meet, interact and start closer collaboration.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: SMLIA2024

Organisers
Takaharu Yaguchi (Kobe University, Japan)
Naonori Ueda (RIKEN, Japan)
Mizuka Komatsu (Kobe University, Japan)
Yuhan Chen (Kobe University, Japan)
Baige Xu (Kobe University, Japan)

Overview
Tensor Networks (TNs), with deep roots in quantum physics, chemistry, and applied mathematics, have demonstrated exceptional performance in handling high-dimensional data, generating multiway structured data, optimising neural network structures and etc. Recently, it is further developed as a potential driving force in advances of machine learning (ML) and artificial intelligence (AI), particularly in crucial areas such as quantum machine learning, trustworthy machine learning, and interpretable machine learning. This workshop aims to bring together researchers from multidisciplinary and discuss not only the fundamental challenges in TNs such as TN structure optimisation, efficient algorithms, and robustness, but also their extended application in addressing key challenges within machine learning, spanning domains such as efficiency, interpretability, reliability and etc. These efforts not only advance a deeper understanding of machine learning models and enhance their reliability but also ensure they meet the demand for resource efficiency, thereby increasing trustworthiness. Furthermore, our workshop is expected to unleash the potential of TNs to open new directions for future research in quantum-inspired machine learning.

Date: To be advised
Time: To be advised
Venue: To be advised

Website: TMME CAI 2024

Organisers
Qibin Zhao (RIKEN AIP, Japan)
Guoxu Zhou (Guangdong University of Technology, China)
Cesar F. Caiafa (University of Buenos Aires, Argentina)
Tatsuya Yokota (Nagoya Institute of Technology, Japan)
Yannan Chen (South China Normal University, China)
Yuning Qiu (RIKEN, Japan)
Andong Wang (RIKEN AIP, Japan)

Overview
This workshop will include an introduction to Gen AI followed by and a hands-on lab session on using Foundation Models and Generative AI.

During the workshop, the participants will be given a good understanding of the concepts and build a couple of custom generative AI applications leveraging powerful foundation models and other AI services available in AWS.

Participants can expect to build:
1) A custom app that integrates to Bedrock models using APIs. We will make custom code, dependencies, configuration, and instructions available for the workshop. Participants are required to bring their own laptops for the lab session.
2) A custom app that uses the RAG (Retrieval-augmented generation) approach and leverages additional services like knowledge base, vector db, etc. in addition to the foundation models

Date: To be advised
Time: To be advised
Venue: To be advised

Organisers
Priscilla Amalraj (IEEE,USA)
Ashok Sivathapandian (IEEE, USA)
Vincent Oh (AWS)