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.
See through Gradients: Image Batch Recovery via GradInversion
2021254 citationsHongxu Yin, Arun Mallya et al.profile →
A-ViT: Adaptive Tokens for Efficient Vision Transformer
2022165 citationsHongxu Yin, Arash Vahdat et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
2023156 citationsSifei Liu, Arash Vahdat et al.profile →
PhysDiff: Physics-Guided Human Motion Diffusion Model
202398 citationsYe Yuan, Jiaming Song et al.profile →
State-specific protein–ligand complex structure prediction with a multiscale deep generative model
202466 citationsZhuoran Qiao, Weili Nie et al.Nature Machine Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Arash Vahdat'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 Arash Vahdat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arash Vahdat more than expected).
This network shows the impact of papers produced by Arash Vahdat. 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 Arash Vahdat. The network helps show where Arash Vahdat may publish in the future.
Co-authorship network of co-authors of Arash Vahdat
This figure shows the co-authorship network connecting the top 25 collaborators of Arash Vahdat.
A scholar is included among the top collaborators of Arash Vahdat 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 Arash Vahdat. Arash Vahdat is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yin, Hongxu, Arun Mallya, Arash Vahdat, et al.. (2021). See through Gradients: Image Batch Recovery via GradInversion. 16332–16341.254 indexed citations breakdown →
10.
Vahdat, Arash, Evgeny Andriyash, & William G. Macready. (2020). Undirected Graphical Models as Approximate Posteriors. International Conference on Machine Learning. 1. 9680–9689.1 indexed citations
11.
Vahdat, Arash & Jan Kautz. (2020). NVAE: A Deep Hierarchical Variational Autoencoder. Neural Information Processing Systems. 33. 19667–19679.18 indexed citations
12.
Vahdat, Arash, et al.. (2018). DVAE++: Discrete Variational Autoencoders with Overlapping Transformations. International Conference on Machine Learning. 5035–5044.2 indexed citations
13.
Vahdat, Arash, Evgeny Andriyash, & William G. Macready. (2018). DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors. arXiv (Cornell University). 31. 1864–1874.3 indexed citations
14.
Vahdat, Arash. (2017). Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks. Neural Information Processing Systems. 30. 5596–5605.68 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.