14 citations · 42 across the 9 of their papers we have counts for
9 papers
Scaling Knowledge Graph Embedding Models
Nasrullah Sheikh, Xiao Qin, Berthold Reinwald +1
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 c…
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
Nasrullah Sheikh, Xiao Qin, Berthold Reinwald +3
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…
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training
Xiao Qin, Nasrullah Sheikh, Berthold Reinwald +1
This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipel…
Forecasting in multivariate irregularly sampled time series with missing values
Shivam Srivastava, Prithviraj Sen, Berthold Reinwald
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regressio…
A Neural Architecture for Person Ontology population
Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh +2
A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for b…
Fine Grained Classification of Personal Data Entities
Riddhiman Dasgupta, Balaji Ganesan, Aswin Kannan +2
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents. While neural networks have recently improved the classification…
Deep Learning with Apache SystemML
Niketan Pansare, Michael Dusenberry, Nakul Jindal +3
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 s…
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML
Matthias Boehm, Berthold Reinwald, Dylan Hutchison +2
Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by means of linear algebra programs, and then automatically generate efficient execution plans.…
Declarative Machine Learning - A Classification of Basic Properties and Types
Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare +1
Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. T…