H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 52 Citations 13,984 273 World Ranking 2582 National Ranking 115

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His main research concerns Artificial intelligence, Machine learning, Gaussian process, Pattern recognition and Statistical relational learning. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Algorithm and Natural language processing. His Machine learning research incorporates elements of Probabilistic logic and Data mining.

His work deals with themes such as Mixture model and Cluster analysis, which intersect with Data mining. The concepts of his Pattern recognition study are interwoven with issues in Weighting, Mixture of experts and Kernel. His research integrates issues of Tensor, Factorization, Knowledge base, Generalization error and Pattern recognition in his study of Semi-supervised learning.

His most cited work include:

  • A Three-Way Model for Collective Learning on Multi-Relational Data (1007 citations)
  • A Review of Relational Machine Learning for Knowledge Graphs (729 citations)
  • Learning Gaussian processes from multiple tasks (351 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Machine learning, Artificial neural network, Data mining and Theoretical computer science are his primary areas of study. His Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition. The various areas that Volker Tresp examines in his Natural language processing study include Image and Information retrieval.

His research in Machine learning intersects with topics in Bayesian probability and Statistical relational learning. His Artificial neural network research integrates issues from Function and Algorithm. His study looks at the intersection of Theoretical computer science and topics like Generalization with Projection.

He most often published in these fields:

  • Artificial intelligence (59.23%)
  • Machine learning (34.16%)
  • Artificial neural network (15.43%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (59.23%)
  • Machine learning (34.16%)
  • Knowledge graph (10.74%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Artificial intelligence, Machine learning, Knowledge graph, Theoretical computer science and Reinforcement learning. His Artificial intelligence study integrates concerns from other disciplines, such as Natural language processing and Pattern recognition. His Deep learning study, which is part of a larger body of work in Machine learning, is frequently linked to Class, bridging the gap between disciplines.

In his research, Representation is intimately related to Benchmark, which falls under the overarching field of Knowledge graph. His biological study spans a wide range of topics, including Embedding, Generalization and Graph. His Artificial neural network study combines topics from a wide range of disciplines, such as Probabilistic logic and Anomaly detection.

Between 2017 and 2021, his most popular works were:

  • Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration (36 citations)
  • Explaining Therapy Predictions with Layer-Wise Relevance Propagation in Neural Networks (35 citations)
  • Understanding Individual Decisions of CNNs via Contrastive Backpropagation (32 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Machine learning, Knowledge graph, Reinforcement learning and Theoretical computer science. Backpropagation, Convolutional neural network, Feature, Object and Artificial neural network are subfields of Artificial intelligence in which his conducts study. His work blends Machine learning and Curiosity studies together.

Volker Tresp has included themes like Text generation, Natural language processing and Benchmark in his Knowledge graph study. His research in Reinforcement learning tackles topics such as Dialog box which are related to areas like Recurrent neural network. His studies in Theoretical computer science integrate themes in fields like Cauchy distribution, Generalization and Graph.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A Three-Way Model for Collective Learning on Multi-Relational Data

Maximilian Nickel;Volker Tresp;Hans-peter Kriegel.
international conference on machine learning (2011)

1049 Citations

A Review of Relational Machine Learning for Knowledge Graphs

Maximilian Nickel;Kevin Murphy;Volker Tresp;Evgeniy Gabrilovich.
Proceedings of the IEEE (2016)

1002 Citations

Learning Gaussian processes from multiple tasks

Kai Yu;Volker Tresp;Anton Schwaighofer.
international conference on machine learning (2005)

454 Citations

A Bayesian Committee Machine

Volker Tresp.
Neural Computation (2000)

435 Citations

Probabilistic memory-based collaborative filtering

Kai Yu;A. Schwaighofer;V. Tresp;Xiaowei Xu.
IEEE Transactions on Knowledge and Data Engineering (2004)

432 Citations

Factorizing YAGO: scalable machine learning for linked data

Maximilian Nickel;Volker Tresp;Hans-Peter Kriegel.
the web conference (2012)

377 Citations

Active learning via transductive experimental design

Kai Yu;Jinbo Bi;Volker Tresp.
international conference on machine learning (2006)

345 Citations

Method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior

Thomas Briegel;Volker Tresp.
(1998)

321 Citations

Multi-label informed latent semantic indexing

Kai Yu;Shipeng Yu;Volker Tresp.
international acm sigir conference on research and development in information retrieval (2005)

297 Citations

Extraction of semantic biomedical relations from text using conditional random fields

Markus Bundschus;Markus Bundschus;Mathaeus Dejori;Mathaeus Dejori;Martin Stetter;Volker Tresp.
BMC Bioinformatics (2008)

264 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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