机构
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McGovern Medical School(麦戈文医学学院)
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McWilliams School of Biomedical Informatics(麦克威廉斯生物医学信息学学院)
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The University of Texas Health Science Center at Houston(德克萨斯大学健康科学中心休斯顿分校)
机构
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Faculty of Science, Agriculture, and Engineering, Newcastle University Singapore(新加坡纽卡斯尔大学科学、农业与工程学院)
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School of Information and Control Engineering, Qingdao University of Technology(青岛理工大学信息与控制工程学院)
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Department of EEE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham(阿姆瑞塔工程学院电气与电子工程系,阿姆瑞塔大学)
ATLAS: A Foundation Neural Sampler for Amorphous Materials
ATLAS:一种用于非晶材料的基础神经采样器
Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du
机构
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Microsoft Research New England(微软研究院新英格兰分部)
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Center for Computational Science and Engineering(计算科学与工程中心)
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MIT(麻省理工学院)
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Karlsruhe Institute of Technology(卡尔斯鲁厄理工学院)
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Department of Materials Science and Engineering(材料科学与工程系)
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Department of Computer Science and Engineering(计算机科学与工程系)
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OSU(俄亥俄州立大学)
A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
人工智能生命周期治理中可审计可信度水平的一种方法
Andrea Ferrario
机构
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Institute of Biomedical Ethics and History of Medicine, University of Zürich(生物医学伦理与医学史研究所,苏黎世大学)
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SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA)(SUPSI人工智能研究所)
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ETH Zürich(苏黎世联邦理工学院)
CityLLM: A framework for natural-language querying of semantic 3D city models
CityLLM:一种用于语义 3D 城市模型自然语言查询的框架
Rabindra Lamsal, Sisi Zlatanova, Johnson Xuesong Shen
机构
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GRID Lab, School of Built Environment, UNSW Sydney(新南威尔士大学悉尼分校建筑环境学院GRID实验室)
;
School of Civil and Environmental Engineering, UNSW Sydney(新南威尔士大学悉尼分校土木与环境工程学院)
专题命中
工作流自动化
:workflow(abstract);分类 cs.CL
AI总结
研究针对语义 3D 城市模型访问查询难的问题,提出 CityLLM 框架,结合空间与图形数据库,在基于语言模型工作流中支持迭代查询等。通过多种模型在鹿特丹数据集上评估,多个场景 54 个查询显示其性能强大,为语义 3D 城市数据对话访问提供轻量级可扩展方法。
CommentsAccepted to the 21st International 3D GeoInfo Conference. To appear in the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
在训练和微调机器学习力场时使用更少标签实现全数据精度
Sheng Bi, Yi-Ze Wang, Jun Cheng
机构
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College of Materials(材料学院)
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Xiamen University(厦门大学)
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State Key Laboratory of Physical Chemistry of Solid Surfaces(固体表面物理化学国家重点实验室)
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iChEM, College of Chemistry and Chemical Engineering(iChEM,化学与化工学院)
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Laboratory of AI for Electrochemistry (AI4EC)(电化学人工智能实验室)
;
IKKEM
A POS Tier Is the Key to Automated Annotation for Low-Resource Language Documentation: Neural Interlinear Glossing for Irabu, a Southern Ryukyuan Language
Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences
用于从图像序列进行端到端认知地图学习的可微克隆结构因果图
Arash Nikzad, Sasan Sarbishegi, Ali Dasmeh, Muhammad Asif, Parsa Gharavi, Erik Husom, Sagar Sen, Andrew B. Lehr, Olivier Penacchio, Ana Clemente, Tristan M. Stöber
机构
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Goethe University Frankfurt(歌德大学法兰克福分校)
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Max Planck Institute for Human Development(马克斯·普朗克人类发展研究所)
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Max Planck Institute for Empirical Aesthetics(马克斯·普朗克实验美学研究所)
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SINTEF(挪威科技工业研究院)
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University Medical Center Göttingen(哥廷根大学医学中心)
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Institute of Computer Science and Campus Institute Data Science, University Göttingen(哥廷根大学计算机科学与数据科学研究所)
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Bridging Research in AI and Neuroscience (brAIN), Computer Vision Center(人工智能与神经科学交叉研究中心(brAIN),计算机视觉中心)
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Computer Science Department, Universitat Autònoma de Barcelona(巴塞罗那自治大学计算机科学系)
;
Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University Frankfurt(歌德大学法兰克福分校莱茵-美因癫痫中心,神经学系)
;
Circulant Labs(循环实验室)
Relaxing Faithfulness with Intervention-Only Causal Discovery
通过仅干预因果发现放松忠实性
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
机构
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Thayer School of Engineering Dartmouth College(达特茅斯学院塞耶工程学院)
;
Broad Institute of MIT and Harvard(麻省理工学院和哈佛大学布罗德研究所)
;
Massachusetts Institute of Technology(麻省理工学院)
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
我们准备好迎接人工智能驱动的发现了吗?在下一次基础物理学突破之前进行人工智能验证
Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
机构
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NSF AI Institute for Artificial Intelligence and Fundamental Interactions(NSF人工智能与基本相互作用研究院)
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MIT Laboratory for Nuclear Science(MIT核科学实验室)
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School of Engineering and Applied Sciences, Harvard University(哈佛大学工程与应用科学学院)
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Nagoya University(名古屋大学)
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Technical University Munich(慕尼黑技术大学)
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Munich Center for Machine Learning (MCML)(慕尼黑机器学习中心)
When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
当路线耗尽时:量子中继器网络中的对抗协同学习与可解释鲁棒性
Brennan Bell, Inti Gabriel Mendoza Estrada, Andreas Trügler, Paul Erker
机构
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RFI-IRFOS and TU Graz(RFI-IRFOS和格拉茨技术大学)
;
openmaind FlexCo and TU Graz(openmaind FlexCo和格拉茨技术大学)
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Know Center Research GmbH and University of Graz(Know Center Research GmbH和格拉茨大学)
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Atominstitut, TU Wien and IQOQI, ÖAW(原子院、维也纳技术大学和ÖAW IQOQI)