Bernd Waschneck

725 total citations · 1 hit paper
16 papers, 514 citations indexed

About

Bernd Waschneck is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering. According to data from OpenAlex, Bernd Waschneck has authored 16 papers receiving a total of 514 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Industrial and Manufacturing Engineering. Recurrent topics in Bernd Waschneck's work include Scheduling and Optimization Algorithms (6 papers), Advanced Neural Network Applications (6 papers) and Parallel Computing and Optimization Techniques (4 papers). Bernd Waschneck is often cited by papers focused on Scheduling and Optimization Algorithms (6 papers), Advanced Neural Network Applications (6 papers) and Parallel Computing and Optimization Techniques (4 papers). Bernd Waschneck collaborates with scholars based in Germany and Canada. Bernd Waschneck's co-authors include Thomas Altenmüller, Andreas Kyek, Thomas Bauernhansl, Lenz Belzner, André Reichstaller, Alexander Knapp, Gisela Lanza, Andreas Kuhnle, Marvin Carl May and Akash Kumar and has published in prestigious journals such as IEEE Access, ACM Computing Surveys and Journal of Manufacturing Systems.

In The Last Decade

Bernd Waschneck

14 papers receiving 497 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optimization of global production scheduling with deep reinforcement learning

2018 264 citations Bernd Waschneck, André Reichstaller et al. Procedia CIRP profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bernd Waschneck Germany	6	388	93	71	43	41	16	514
Lenz Belzner Germany	7	444 1.1×	89 1.0×	112 1.6×	72 1.7×	37 0.9×	29	620
André Reichstaller Germany	4	282 0.7×	65 0.7×	64 0.9×	42 1.0×	28 0.7×	8	366
Jianlin Fu China	4	512 1.3×	64 0.7×	50 0.7×	54 1.3×	37 0.9×	8	584
André Dionísio Rocha Portugal	13	340 0.9×	42 0.5×	36 0.5×	43 1.0×	56 1.4×	43	473
Janis S. Neufeld Germany	16	681 1.8×	86 0.9×	65 0.9×	74 1.7×	25 0.6×	24	747
Minghai Yuan China	14	579 1.5×	74 0.8×	45 0.6×	72 1.7×	19 0.5×	51	724
Miguel Saez United States	9	150 0.4×	62 0.7×	46 0.6×	41 1.0×	18 0.4×	13	292
Marc Priggemeyer Germany	4	300 0.8×	65 0.7×	20 0.3×	54 1.3×	16 0.4×	11	426

Countries citing papers authored by Bernd Waschneck

Since Specialization

Citations

This map shows the geographic impact of Bernd Waschneck'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 Bernd Waschneck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernd Waschneck more than expected).

Fields of papers citing papers by Bernd Waschneck

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernd Waschneck. 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 Bernd Waschneck. The network helps show where Bernd Waschneck may publish in the future.

Co-authorship network of co-authors of Bernd Waschneck

This figure shows the co-authorship network connecting the top 25 collaborators of Bernd Waschneck. A scholar is included among the top collaborators of Bernd Waschneck 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 Bernd Waschneck. Bernd Waschneck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Servadei, Lorenzo, et al.. (2024). Leveraging Temporal Patterns: Automated Augmentation to Create Temporal Early Exit Networks for Efficient Edge AI. IEEE Access. 12. 169787–169804.

Servadei, Lorenzo, et al.. (2024). Harnessing Temporal Information for Efficient Edge AI. 5–13. 2 indexed citations

Waschneck, Bernd, et al.. (2024). Automating application-driven customization of ASIPs: A survey. Journal of Systems Architecture. 148. 103080–103080. 2 indexed citations

Waschneck, Bernd, et al.. (2024). Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning. ACM Computing Surveys. 56(10). 1–40. 9 indexed citations

Waschneck, Bernd, et al.. (2023). High-Throughput Approximate Multiplication Models in PyTorch. 79–82. 1 indexed citations

Waschneck, Bernd, et al.. (2022). AI-Driven Performance Modeling for AI Inference Workloads. Electronics. 11(15). 2316–2316. 3 indexed citations

Waschneck, Bernd, et al.. (2022). Industry-track: Towards Agile Design of Neural Processing Unit. 17–20.

Altenmüller, Thomas, et al.. (2022). Opportunistic maintenance scheduling with deep reinforcement learning. Journal of Manufacturing Systems. 64. 518–534. 51 indexed citations

Waschneck, Bernd, et al.. (2022). Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks. 1–8. 2 indexed citations

10.

Waschneck, Bernd, et al.. (2022). Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold. International Journal of Neural Systems. 32(12). 2250051–2250051. 5 indexed citations

11.

Waschneck, Bernd, et al.. (2021). dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference. 1–9. 5 indexed citations

12.

Altenmüller, Thomas, et al.. (2020). Reinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints. Production Engineering. 14(3). 319–328. 56 indexed citations

13.

Waschneck, Bernd, André Reichstaller, Lenz Belzner, et al.. (2018). Deep reinforcement learning for semiconductor production scheduling. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 301–306. 84 indexed citations

14.

Waschneck, Bernd, Thomas Altenmüller, Thomas Bauernhansl, & Andreas Kyek. (2018). Case Study on Operator Compliance to Scheduling Decisions in Semiconductor Manufacturing. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 649–652. 1 indexed citations

15.

Waschneck, Bernd, André Reichstaller, Lenz Belzner, et al.. (2018). Optimization of global production scheduling with deep reinforcement learning. Procedia CIRP. 72. 1264–1269. 264 indexed citations breakdown →

16.

Waschneck, Bernd, Thomas Bauernhansl, Thomas Altenmüller, & Andreas Kyek. (2017). Production Scheduling in Complex Job Shops from an Industrie 4.0 Perspective: A Review and Challenges in the Semiconductor Industry. Zenodo (CERN European Organization for Nuclear Research). 29 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.

Explore authors with similar magnitude of impact