2nd International Workshop on Generalizing from
Limited Resources in the Open World
Workshop at International Joint Conference on Artificial Intelligence (IJCAI) 2024
Overview
The capacity for flexible and efficient generalization from
limited resources is a hallmark of human intelligence.
However, artificial intelligence (AI) models often encounter
challenges in extending their capabilities from
limited resources like constrained training data and computation,
particularly in fields such as computer vision
and natural language processing. The struggle to generalize
effectively from limited resources hinders the practical
deployment of AI models in the open world, where
efficient learning from sparse samples in unpredictable
scenarios is essential. Recognizing this imperative, our
workshop aims to delve into the intricate landscape of
AI model generalization in open-world settings to foster
advancements in real-world AI applications. Numerous
endeavors have been undertaken to surmount these
challenges, addressing the inefficiencies in learning models
and bridging the divide between human and machine
intelligence.
This workshop centers on the academic exploration
of efficient methodologies within the realm of artificial
intelligence (AI) models. Our focus encompasses both
data-efficient strategies, such as zero/few-shot learning
and domain adaptation, as well as model-efficient approaches
like model sparsification and compact model
design. By convening researchers specializing in these
areas, our objective is to facilitate the sharing of recent
research findings and engage in discussions about the
future trajectories of AI model generalization. The significance
of this workshop is underscored by the timely
relevance of its thematic focus, which has garnered substantial
attention from the research community due to its
direct implications for practical applications. Through
this forum, we aspire to foster the exchange of ideas,
providing an avenue for the articulation of novel insights
aimed at addressing the generalization challenges confronting
AI models, thereby contributing to the advancement
of real-world AI applications.