no code implementations • 19 May 2016 • Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald
Specification alternatives range from ML algorithms expressed in domain-specific languages (DSLs) with optimization for performance, to ML task (learning problem) specifications with optimization for performance and accuracy.
no code implementations • 8 Feb 2018 • Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning.
no code implementations • 23 Nov 2018 • Riddhiman Dasgupta, Balaji Ganesan, Aswin Kannan, Berthold Reinwald, Arun Kumar
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents.
no code implementations • 22 Jan 2020 • Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh, Hima Patel, Berthold Reinwald
A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention.
no code implementations • 6 Apr 2020 • Shivam Srivastava, Prithviraj Sen, Berthold Reinwald
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains.
1 code implementation • COLING 2020 • Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
Network representation learning (NRL) is crucial in the area of graph learning.
no code implementations • 14 Feb 2021 • Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Christoph Miksovic, Thomas Gschwind, Paolo Scotton
Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and entity matching.
no code implementations • 14 Feb 2021 • Xiao Qin, Nasrullah Sheikh, Berthold Reinwald, Lingfei Wu
Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training.
no code implementations • 8 Jan 2022 • Nasrullah Sheikh, Xiao Qin, Berthold Reinwald, Chuan Lei
Developing scalable solutions for training Graph Neural Networks (GNNs) for link prediction tasks is challenging due to the high data dependencies which entail high computational cost and huge memory footprint.