Efficient Learning Algorithms with Limited Resources

Dear Colleagues,

Machine Learning has recently achieved significant accomplishments across a diverse array of application domains (e.g., computer vision). Nonetheless, these achievements are heavily contingent upon substantial reservoirs of data and computational resources. This dependence poses a challenge in most real-world scenarios where data and computation resources are scarce. Our goal is to confront this challenge by devising effective strategies for implementing machine learning under conditions of limited resources, encompassing data, models, and knowledge. Consequently, researchers across various fields have turned their attention to the exploration of efficient learning methodologies. These methodologies encompass three key dimensions:

1. Efficient data processing algorithm, which involves techniques such as lossy or lossy coding.
2. Efficient model processing algorithm, which practices such as channel pruning and neural architecture search to enhance computational efficiency.
3. Efficient knowledge transferring algorithm, as exemplified by transfer learning techniques including knowledge distillation that leverage existing knowledge effectively.

We extend an invitation to experts not only from these specific domains but also from related fields to engage in collaborative efforts and put forth groundbreaking methodologies. We hold a particular interest in receiving proposals that compose multiple themes mentioned above. For instance, we strongly encourage the exploration of integrated approaches that merge data and machine efficiency, employing compressed networks to reduce data volume. We believe such innovative methodologies harbor the potential to reshape the trajectory of machine learning and its applications, spanning realms such as computer vision and natural language processing. By addressing the formidable challenges posed by resource limitations, we aspire to make substantial contributions to the broader research community.

Dr. Luping Zhou
Guest Editor

Dr. Zhenghao Chen
Guest Editor Assistant

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