Mantas Mazeika

3.8k total citations · 1 hit paper
5 papers, 442 citations indexed

About

Mantas Mazeika is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mantas Mazeika has authored 5 papers receiving a total of 442 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 1 paper in Computer Networks and Communications and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Mantas Mazeika's work include Anomaly Detection Techniques and Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Mantas Mazeika is often cited by papers focused on Anomaly Detection Techniques and Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Mantas Mazeika collaborates with scholars based in United States and Jamaica. Mantas Mazeika's co-authors include Dan Hendrycks, Dawn Song, Jacob Steinhardt, Thomas G. Dietterich, Andy Zou, Steven Basart, Collin Burns, Saurav Kadavath, Leonard Tang and Bo Li and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Mantas Mazeika

5 papers receiving 419 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.

Measuring Massive Multitask Language Understanding

2021 182 citations Dan Hendrycks, Collin Burns et al. International Conference on Learning Representations profile →

Peers

Countries citing papers authored by Mantas Mazeika

Since Specialization

Citations

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

Fields of papers citing papers by Mantas Mazeika

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mantas Mazeika

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

5 of 5 papers shown

#	Work	Indexed citations
1	PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures 2022 ·2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ·Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt	48
2	Measuring Massive Multitask Language Understanding breakdown → 2021 ·International Conference on Learning Representations ·Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt	182
3	A Benchmark for Anomaly Segmentation. 2019 ·arXiv (Cornell University) ·Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, Dawn Song	23
4	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty 2019 ·Neural Information Processing Systems ·Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song	77
5	Deep Anomaly Detection with Outlier Exposure 2018 ·arXiv (Cornell University) ·Dan Hendrycks, Mantas Mazeika, Thomas G. Dietterich	112

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