Merging datasets is a key operation for data analytics. A frequent requirement for merging is joining across columns that have different surface forms for the same entity (e.g., the name of a person might be represented as "Douglas Adams" or "Adams, Douglas"). Similarly, ontology alignment can require recognizing distinct surface forms of the same entity, especially when ontologies are independently developed. However, data management systems are currently limited to performing merges based on string equality, or at best using string similarity. We propose an approach to performing merges based on deep learning models. Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together. To build these models, we had to adapt techniques from metric learning due to the characteristics of the data; specifically we describe novel sample selection techniques and loss functions that work for this problem. To evaluate our approach, we used Wikidata as ground truth and built models from datasets with approximately 1.1M people's names (200K identities) and 130K company names (70K identities). We developed models that allow for joins with precision@1 of .75-.81 and recall of .74-.81. We make the models available for aligning people or companies across multiple datasets.
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
Fermi National Accelerator Laboratory（费米国家加速器实验室）；University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；Space Telescope Science Institute（太空望远镜科学研究所）；Kavli Institute for Cosmological Physics（卡夫利宇宙物理研究所）
DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation
Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；University of Twente（特文特大学）；Wuhan University（武汉大学）；University of Arkansas（阿肯色大学）；China University of Geosciences (Beijing)（中国地质大学（北京））
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
College of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Rensselaer Polytechnic Institute（拉特盖尔理工学院）；College of applied Science, Taiyuan University of Science and Technology（太原科技大学应用科学学院）；CSIRO’s Data61（CSIRO数据61）
Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Horizon Robotics（地平线机器人）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
Massachusetts General Hospital（麻省总医院）；Dana Farber Cancer Institute（丹娜法伯癌症研究院）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Boston University School of Public Health（波士顿大学公共卫生学院）
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery
Northeastern University（东北大学）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络与空间科学学院）；University of Maryland（马里兰大学）；Nanyang Technological University（南洋理工大学）
针对模型合并多任务学习中的表征偏差问题，本文提出 Surgery 和 SurgeryV2 模块，通过逐层对齐合并模型与专家模型的表征，显著缩小与传统多任务学习的性能差距。
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Technion-IIT（以色列理工学院）；École Polytechnique, Institut Polytechnique de Paris（巴黎理工学院综合理工学院）；Uppsala University（乌普萨拉大学）；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
How does the optimizer implicitly bias the model merging loss landscape?
University of Luxembourg（卢森堡大学）；ETH Zürich, MPI for Intelligent Systems（苏黎世联邦理工学院、智能系统研究所）；Idiap Research Institute（Idiap研究机构）；ELLIS Institute Tübingen, MPI for Intelligent Systems, Tübingen AI Center（图宾根ELLIS研究所、智能系统研究所、图宾根人工智能中心）
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
School of Computer Science & Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)（华中科技大学与平安产险联合实验室）；Ping An Property & Casualty Insurance Company of China, Ltd.（中国平安财产保险股份有限公司）；The Chinese University of Hong Kong（香港中文大学）
