Jiaming Song

1.0k total citations
13 papers, 184 citations indexed

About

Jiaming Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Jiaming Song has authored 13 papers receiving a total of 184 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 4 papers in Signal Processing. Recurrent topics in Jiaming Song's work include Generative Adversarial Networks and Image Synthesis (3 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Time Series Analysis and Forecasting (2 papers). Jiaming Song is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (3 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Time Series Analysis and Forecasting (2 papers). Jiaming Song collaborates with scholars based in China, United States and Sweden. Jiaming Song's co-authors include Stefano Ermon, Shengjia Zhao, Volodymyr Kuleshov, Hongyu Ren, Russell J. Stewart, Mykel J. Kochenderfer, Jochen Schröder, Laurent Schmalen, Giuseppe Buja and Kun Ho Kim and has published in prestigious journals such as Signal Processing, AI Magazine and Tsinghua Science & Technology.

In The Last Decade

Jiaming Song

9 papers receiving 177 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jiaming Song China	4	104	81	22	18	14	13	184
Thomas Kipf Netherlands	7	120 1.2×	87 1.1×	32 1.5×	15 0.8×	7 0.5×	10	184
Albert Gu United States	8	78 0.8×	38 0.5×	11 0.5×	9 0.5×	7 0.5×	23	170
Benjamin Eysenbach United States	4	86 0.8×	78 1.0×	17 0.8×	9 0.5×	20 1.4×	6	133
Tianjun Xiao China	7	147 1.4×	138 1.7×	22 1.0×	8 0.4×	9 0.6×	13	235
Hongyin Luo China	7	164 1.6×	55 0.7×	10 0.5×	4 0.2×	13 0.9×	27	231
Hongyang Zhang China	7	178 1.7×	56 0.7×	17 0.8×	3 0.2×	5 0.4×	29	227
Chengtai Cao China	6	208 2.0×	66 0.8×	16 0.7×	4 0.2×	4 0.3×	9	255
Lingyun Song China	10	159 1.5×	120 1.5×	16 0.7×	3 0.2×	4 0.3×	25	257
Xiangfeng Wang China	6	63 0.6×	60 0.7×	7 0.3×	6 0.3×	13 0.9×	20	171
Xitong Gao China	8	104 1.0×	92 1.1×	17 0.8×	3 0.2×	13 0.9×	20	208

Countries citing papers authored by Jiaming Song

Since Specialization

Citations

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

Fields of papers citing papers by Jiaming Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiaming Song

This figure shows the co-authorship network connecting the top 25 collaborators of Jiaming Song. A scholar is included among the top collaborators of Jiaming Song 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 Jiaming Song. Jiaming Song 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:

13 of 13 papers shown

1.

Zhang, Lei, et al.. (2024). Comprehensive Resilience Assessment of Distribution Network In Extreme Weather. 713–718.

2.

Cheng, Ting, et al.. (2024). Adaptive Dwell Scheduling Based on Dual-Side Time Pointers for Simultaneous Multi-Beam Radar. Tsinghua Science & Technology. 30(3). 1190–1200.

3.

Wang, Qingsong, et al.. (2024). A Scheduling Algorithm for Mobile Charging Devices in Distribution System Considering the Comprehensive Influence of Multiple Factors. 744–749. 1 indexed citations

4.

Song, Jiaming, et al.. (2024). LPI-based resource allocation strategy for multiple targets tracking in CMIMO radar system with array division. Signal Processing. 225. 109625–109625. 1 indexed citations

5.

Song, Jiaming, et al.. (2023). Blind frequency-domain equalization using vector-quantized variational autoencoders. IET conference proceedings.. 2023(34). 1222–1225. 1 indexed citations

6.

Ermon, Stefano, et al.. (2022). Denoising Diffusion Restoration Models. 23593–23606.

7.

Song, Jiaming, et al.. (2020). Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. 1855–1857. 5 indexed citations

8.

Kim, Kun Ho, et al.. (2019). Cross Domain Imitation Learning. arXiv (Cornell University). 1 indexed citations

9.

Zhao, Shengjia, Jiaming Song, & Stefano Ermon. (2019). InfoVAE: Balancing Learning and Inference in Variational Autoencoders. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 5885–5892. 118 indexed citations

10.

Zhao, Shengjia, Jiaming Song, & Stefano Ermon. (2018). A Lagrangian Perspective on Latent Variable Generative Models. Uncertainty in Artificial Intelligence. 1031–1041. 1 indexed citations

11.

Ren, Hongyu, Russell J. Stewart, Jiaming Song, Volodymyr Kuleshov, & Stefano Ermon. (2018). Adversarial Constraint Learning for Structured Prediction. 2637–2643. 3 indexed citations

12.

Ren, Hongyu, Russell J. Stewart, Jiaming Song, Volodymyr Kuleshov, & Stefano Ermon. (2018). Learning with Weak Supervision from Physics and Data‐Driven Constraints. AI Magazine. 39(1). 27–38. 16 indexed citations

13.

Zhao, Shengjia, Jiaming Song, & Stefano Ermon. (2017). Learning Hierarchical Features from Deep Generative Models. International Conference on Machine Learning. 4091–4099. 37 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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