AAAI-MAKE 2024 brings together a diverse community of researchers, practitioners, and industry professionals from the fields of machine learning, knowledge engineering, and large language models (LLMs) to explore the synergy between these domains. The symposium aims to address the critical challenges o...
The AAAI-MAKE 2023 symposium brings together researchers and practitioners from machine learning and knowledge engineering to reflect on how combining the two fields can contribute to tackling future societal, environmental, business, and fundamental AI challenges. It aims to provide datasets, ontologies,...
The AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) brought together researchers and practitioners from the machine learning and knowledge engineering fields to reflect two years later the progress on the combination of machine learning and knowledge ...
The AAAI-MAKE 2022 symposium aimed to bring together researchers and practitioners from machine learning and knowledge engineering to reflect how combining the two fields can contribute to hybrid intelligence systems. The symposium was scheduled on March 21-23, 2022 at Stanford University, Palo Alto, ...
Make AI Everywhere,腾讯AI亮相AAAI-17国际会议 AAAI,中国力量在崛起 AAAI是人工智能领域的最主要学术组织之一,它由计算机科学和人工智能的创始人 Allen Newell, Marvin Minsky和John McCarthy 等人于1979年创立,当时的全称是The American Association for Artificial Intelligence(美国人工智能学会),并于2007年更名为The ...
CTR(click-through rate)预测模型是个性化推荐场景下的一种常用算法,它通常需要学习用户的反馈(点击、收藏、购买等),而每天在线产生的数据量又是空前庞大的。因此,加快 CTR 预估模型的训练速度至关重要。一般来说,提高训练速度会使用批量训练,不过批量太大会导致模型的准确度有所降低。
Moreover, the unique challenges brought by deep reinforcement learning (DRL) make the attack even more challenging. To address these challenges, MAFL is designed with a two-stage attacking mechanism. Using two representative attack cases with real-world traces, we show that MAFL significantly de...
bandwidth consumption. Aggregating the parameters that are similar across the clients does not make ...
备注| Submitted to AAAI-MAKE 2020 [312] Weakly-Supervised Opinion Summarization by Leveraging External Information 链接| https://arxiv.org/pdf/1911.09844 分类| cs.CL 备注| Accepted By AAAI-20 [313] Differentiable Algorithm for Marginalising Changepoints ...
作者的方法能够检测出一些难以「检测」的物体来生成精准的描述，比如 (b) 中用来化妆的小物体眉笔，比如 (d) 中被严重遮挡的人，分别通过先验知识<woman,put_on,makeup>和<woman,play_with,cat>推断了出来。并且该方法也能生成中文描述如 (c) 和 (f)，圆括号中的英文是对中文的翻译。论文方法 C-R ...