Maksims Volkovs

2.0k total citations · 1 hit paper
41 papers, 1.1k citations indexed

About

Maksims Volkovs is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Maksims Volkovs has authored 41 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 17 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Maksims Volkovs's work include Recommender Systems and Techniques (14 papers), Topic Modeling (8 papers) and Multimodal Machine Learning Applications (6 papers). Maksims Volkovs is often cited by papers focused on Recommender Systems and Techniques (14 papers), Topic Modeling (8 papers) and Multimodal Machine Learning Applications (6 papers). Maksims Volkovs collaborates with scholars based in Canada, United States and Denmark. Maksims Volkovs's co-authors include Richard S. Zemel, Guang Yu, Tomi Poutanen, Animesh Garg, Keyvan Golestan, Himanshu Rai, Fei Chiang, Jianing Sun, Jaroslaw Szlichta and Renée J. Miller and has published in prestigious journals such as Nature Communications, Journal of Machine Learning Research and BMJ Open.

In The Last Decade

Maksims Volkovs

38 papers receiving 1.1k 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.

Decentralized federated learning through proxy model sharing

2023 73 citations Shivam Kalra, Jesse C. Cresswell et al. Nature Communications profile →

Peers

Countries citing papers authored by Maksims Volkovs

Since Specialization

Citations

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

Fields of papers citing papers by Maksims Volkovs

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maksims Volkovs

This figure shows the co-authorship network connecting the top 25 collaborators of Maksims Volkovs. A scholar is included among the top collaborators of Maksims Volkovs 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 Maksims Volkovs. Maksims Volkovs 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

#	Work	Indexed citations
1	Data-Efficient Multimodal Fusion on a Single GPU 2024 ·(unknown), Zhaoyan Liu, (unknown), (unknown), Jesse C. Cresswell, (unknown), (unknown), Maksims Volkovs	3
2	Decentralized federated learning through proxy model sharing breakdown → 2023 ·Nature Communications ·Shivam Kalra, (unknown), Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh	73
3	DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation 2023 ·(unknown), (unknown), (unknown), Maksims Volkovs	0
4	Approximating femoral neck bone mineral density from hand, knee, and pelvis X-rays using deep learning 2023 ·Keyvan Golestan, Catriona Syme, Alexander Bilbily, Saba Zuberi, Maksims Volkovs, Tomi Poutanen, Mark Cicero	0
5	Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study 2022 ·BMJ Open ·(unknown), Vinyas Harish, Jahir M. Gutierrez, Mathieu Ravaut, Kathy Kornas, Tristan Watson, Tomi Poutanen, Marzyeh Ghassemi, Maksims Volkovs, Laura C. Rosella	8
6	Session-based Recommendation with Transformers 2022 ·(unknown), Zhaolin Gao, (unknown), Jianing Sun, Bradley Brown, Guangwei Yu, (unknown), (unknown), Maksims Volkovs	2
7	Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data 2021 ·npj Digital Medicine ·Mathieu Ravaut, (unknown), (unknown), Maksims Volkovs, Kathy Kornas, Vinyas Harish, Tristan Watson, Gary F. Lewis, Alanna Weisman, Tomi Poutanen, Laura C. Rosella	71
8	User Engagement Modeling with Deep Learning and Language Models 2021 ·Maksims Volkovs, (unknown), (unknown), Jianing Sun, (unknown), (unknown), (unknown), (unknown)	0
9	Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees 2021 ·CMAJ Open ·Jahir M. Gutierrez, Maksims Volkovs, Tomi Poutanen, Tristan Watson, Laura C. Rosella	7
10	Improving Transformer Optimization Through Better Initialization 2020 ·International Conference on Machine Learning ·(unknown), Juan Manuel Pérez, Jimmy Ba, Maksims Volkovs	23
11	Guided Similarity Separation for Image Retrieval 2019 ·Neural Information Processing Systems ·(unknown), Guang Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, (unknown)	10
12	Explore-Exploit Graph Traversal for Image Retrieval 2019 ·Cheng Chang, (unknown), (unknown), Maksims Volkovs	19
13	Robust contextual models for in-session personalization 2019 ·Maksims Volkovs, (unknown), (unknown), (unknown), (unknown), Yichao Lu	4
14	DropoutNet: Addressing Cold Start in Recommender Systems 2017 ·Neural Information Processing Systems ·Maksims Volkovs, Guang Yu, Tomi Poutanen	86
15	Effective Latent Models for Binary Feedback in Recommender Systems 2015 ·Maksims Volkovs, Guang Yu	41
16	New learning methods for supervised and unsupervised preference aggregation 2014 ·Journal of Machine Learning Research ·Maksims Volkovs, Richard S. Zemel	23
17	Continuous data cleaning 2014 ·Maksims Volkovs, Fei Chiang, Jaroslaw Szlichta, Renée J. Miller	65
18	Collaborative Ranking With 17 Parameters 2012 ·Neural Information Processing Systems ·Maksims Volkovs, Richard S. Zemel	27
19	Efficient Sampling for Bipartite Matching Problems 2012 ·Neural Information Processing Systems ·Maksims Volkovs, Richard S. Zemel	4
20	A flexible generative model for preference aggregation 2012 ·Maksims Volkovs, Richard S. Zemel	33

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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