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GenAI 2025: International Symposium on Generative Artificial Intelligence

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截稿日期:
2025-09-21
通知日期:
2025-10-10
会议日期:
2025-11-25
会议地点:
Vienna, Austria
届数:
2
浏览: 7926   关注: 0   参加: 0

会伴指数 (CP-I)

39.5 / 100
全站第 5,263 名 / 共 5,693 个会议 · 前 93%

人工智能与机器学习 第 676 / 742

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
30
社区关注 (10%)
8
资料公开度 (15%)
35

用到的输入: 有据可查的届次：2 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-05

征稿

GenAI 2025 (International Symposium on Generative Artificial Intelligence) is an academic conference held in Vienna, Austria on 2025-11-25. The paper submission deadline is 2025-09-21. Acceptance notifications are sent on 2025-10-10.

The Symposium on Generative AI (GenAI) is an international forum for researchers, practitioners, and thought leaders to share their latest advancements, insights, and experiences in the field of generative artificial intelligence. As a general-purpose technology with potential for wide impact, GenAI aims to bring together a diverse group of contributors to present and debate the holistic advancements in this domain. We invite submissions that address the core topics of foundation models, innovative applications of large language models (LLMs) in various industries, and the ethical and societal impacts of these technologies, including the challenges and ramifications on the workforce and the larger society. The GenAI 2025 symposium focuses on the fundamental aspects of generative AI, including theoretical advancements, architectural innovations, and performance improvements. Topics of interest include, but are not limited to: Novel neural network architectures for generative models Efficient pre-training algorithms and optimization techniques Investigation of scaling law and emergent abilities of LLMs Explainability techniques in LLMs Novel supervised fine tuning techniques to unlock inherit LLM abilities Benchmark datasets and standardization LLMs deployment challenges on limited resources edge devices Adversarial attacks and defenses in generative models Foundational models for computer vision (LVMs) Fine tuning and Prompt Engineering
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