Robert Peharz

1.3k total citations
28 papers, 473 citations indexed

About

Robert Peharz is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Robert Peharz has authored 28 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 11 papers in Signal Processing and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Robert Peharz's work include Bayesian Modeling and Causal Inference (8 papers), Speech and Audio Processing (7 papers) and Machine Learning and Data Classification (6 papers). Robert Peharz is often cited by papers focused on Bayesian Modeling and Causal Inference (8 papers), Speech and Audio Processing (7 papers) and Machine Learning and Data Classification (6 papers). Robert Peharz collaborates with scholars based in Austria, United Kingdom and Germany. Robert Peharz's co-authors include Franz Pernkopf, Peter B. Marschik, Christa Einspieler, Pedro Domingos, Sebastian Tschiatschek, Florian B. Pokorny, Pejman Mowlaee, Robert Gens, Kristian Kersting and Dajie Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Neurocomputing.

In The Last Decade

Robert Peharz

28 papers receiving 457 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Robert Peharz Austria	10	149	136	109	90	60	28	473
Gerard Sanromà Spain	14	111 0.7×	117 0.9×	274 2.5×	17 0.2×	16 0.3×	28	510
N.B. Karayiannis United States	8	32 0.2×	191 1.4×	217 2.0×	23 0.3×	33 0.6×	18	368
Liangjun Chen China	7	53 0.4×	100 0.7×	71 0.7×	30 0.3×	42 0.7×	23	279
Lili He United States	9	145 1.0×	20 0.1×	130 1.2×	10 0.1×	37 0.6×	23	349
Datian Ye China	12	49 0.3×	32 0.2×	56 0.5×	82 0.9×	21 0.3×	66	504
Steffen Oeltze Germany	13	21 0.1×	93 0.7×	303 2.8×	52 0.6×	92 1.5×	25	558
E.C. Tan Singapore	10	44 0.3×	162 1.2×	167 1.5×	114 1.3×	15 0.3×	32	466
Vamsi Krishna Ithapu United States	10	8 0.1×	114 0.8×	144 1.3×	134 1.5×	60 1.0×	37	457
Jan‐Ray Liao Taiwan	11	17 0.1×	31 0.2×	85 0.8×	19 0.2×	23 0.4×	33	414

Countries citing papers authored by Robert Peharz

Since Specialization

Citations

This map shows the geographic impact of Robert Peharz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Robert Peharz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Peharz more than expected).

Fields of papers citing papers by Robert Peharz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert Peharz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Robert Peharz. The network helps show where Robert Peharz may publish in the future.

Co-authorship network of co-authors of Robert Peharz

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Peharz. A scholar is included among the top collaborators of Robert Peharz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Robert Peharz. Robert Peharz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Quaeghebeur, Erik, et al.. (2023). Continuous Mixtures of Tractable Probabilistic Models. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7244–7252. 3 indexed citations

Molina, Alejandro, et al.. (2021). Conditional sum-product networks: Modular probabilistic circuits via gate functions. International Journal of Approximate Reasoning. 140. 298–313. 2 indexed citations

Zhang, Dajie, Tomas Kulvičius, Sven Bölte, et al.. (2021). Novel AI driven approach to classify infant motor functions. Scientific Reports. 11(1). 9888–9888. 58 indexed citations

Trapp, Martin, Robert Peharz, Franz Pernkopf, & Carl Edward Rasmussen. (2020). Deep Structured Mixtures of Gaussian Processes. Apollo (University of Cambridge). 2251–2261. 2 indexed citations

Peharz, Robert, et al.. (2019). Faster Attend-Infer-Repeat with Tractable Probabilistic Models.. TU/e Research Portal. 5966–5975. 8 indexed citations

Trapp, Martin, Robert Peharz, Hong Ge, Franz Pernkopf, & Zoubin Ghahramani. (2019). Bayesian Learning of Sum-Product Networks. TU/e Research Portal. 32. 6344–6355. 4 indexed citations

Peharz, Robert, et al.. (2019). Hierarchical Decompositional Mixtures of Variational Autoencoders. TU/e Research Portal. 6115–6124. 1 indexed citations

Peharz, Robert, Antonio Vergari, Alejandro Molina, et al.. (2019). Random sum-product networks: A simple and effective approach to probabilistic deep learning. TU/e Research Portal. 334–344. 14 indexed citations

Vergari, Antonio, Robert Peharz, Nicola Di Mauro, et al.. (2018). Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 6 indexed citations

10.

Einspieler, Christa, Robert Peharz, & Peter B. Marschik. (2016). Fidgety movements – tiny in appearance, but huge in impact. Jornal de Pediatria. 92(3). S64–S70. 119 indexed citations

11.

Trapp, Martin, Robert Peharz, Marcin Skowron, et al.. (2016). Structure Inference in Sum-Product Networks using Infinite Sum-Product Trees. 1 indexed citations

12.

Peharz, Robert, et al.. (2015). Representation Learning for Single-Channel Source Separation and Bandwidth Extension. IEEE/ACM Transactions on Audio Speech and Language Processing. 23(12). 2398–2409. 14 indexed citations

13.

Peharz, Robert, et al.. (2015). On representation learning for artificial bandwidth extension. 791–795. 3 indexed citations

14.

Peharz, Robert, Robert Gens, & Pedro Domingos. (2014). Learning Selective Sum-Product Networks. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 76(5). 0–0. 17 indexed citations

15.

Peharz, Robert, et al.. (2014). Modeling speech with sum-product networks: Application to bandwidth extension. 3699–3703. 28 indexed citations

16.

Peharz, Robert, Sebastian Tschiatschek, & Franz Pernkopf. (2013). The Most Generative Maximum Margin Bayesian Networks. Cambridge University Engineering Department Publications Database. 235–243. 3 indexed citations

17.

Peharz, Robert & Franz Pernkopf. (2012). Exact Maximum Margin Structure Learning of Bayesian Networks. arXiv (Cornell University). 771–778. 2 indexed citations

18.

Peharz, Robert & Franz Pernkopf. (2011). Sparse nonnegative matrix factorization with ℓ0-constraints. Neurocomputing. 80(1). 38–46. 115 indexed citations

19.

Peharz, Robert, Michael Wohlmayr, & Franz Pernkopf. (2011). Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization. 5416–5419. 5 indexed citations

20.

Wohlmayr, Michael, Robert Peharz, & Franz Pernkopf. (2011). Efficient implementation of probabilistic multi-pitch tracking. b30. 5412–5415. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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