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.
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks
2016468 citationsPavlo Molchanov, Shalini Gupta et al.profile →
Hand gesture recognition with 3D convolutional neural networks
2015331 citationsPavlo Molchanov, Shalini Gupta et al.profile →
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion
2020284 citationsHongxu Yin, Pavlo Molchanov et al.profile →
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 →
VILA: On Pre-training for Visual Language Models
202463 citationsHongxu Yin, Pavlo Molchanov et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Pavlo Molchanov
Since
Specialization
Citations
This map shows the geographic impact of Pavlo Molchanov'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 Pavlo Molchanov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavlo Molchanov more than expected).
This network shows the impact of papers produced by Pavlo Molchanov. 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 Pavlo Molchanov. The network helps show where Pavlo Molchanov may publish in the future.
Co-authorship network of co-authors of Pavlo Molchanov
This figure shows the co-authorship network connecting the top 25 collaborators of Pavlo Molchanov.
A scholar is included among the top collaborators of Pavlo Molchanov 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 Pavlo Molchanov. Pavlo Molchanov is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yin, Hongxu, Pavlo Molchanov, José M. Alvarez, et al.. (2020). Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion. 8712–8721.284 indexed citations breakdown →
12.
Leroux, Sam, Pavlo Molchanov, Pieter Simoens, et al.. (2018). IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification. Ghent University Academic Bibliography (Ghent University). 1–4.
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Inference. International Conference on Learning Representations.123 indexed citations
16.
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning.. arXiv (Cornell University).197 indexed citations
17.
Molchanov, Pavlo, Shalini Gupta, Kihwan Kim, & Jan Kautz. (2015). Hand gesture recognition with 3D convolutional neural networks. 1–7.331 indexed citations breakdown →
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.