Mark Palatucci

3.6k total citations · 1 hit paper
7 papers, 573 citations indexed

About

Mark Palatucci is a scholar working on Artificial Intelligence, Automotive Engineering and Statistics and Probability. According to data from OpenAlex, Mark Palatucci has authored 7 papers receiving a total of 573 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Automotive Engineering and 2 papers in Statistics and Probability. Recurrent topics in Mark Palatucci's work include Machine Learning and Algorithms (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Statistical Methods and Inference (2 papers). Mark Palatucci is often cited by papers focused on Machine Learning and Algorithms (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Statistical Methods and Inference (2 papers). Mark Palatucci collaborates with scholars based in United States, Germany and Canada. Mark Palatucci's co-authors include Tom M. Mitchell, Dean Pomerleau, Geoffrey E. Hinton, Jian Zhang, Han Liu, Alona Fyshe, Leila Wehbe, Riitta Salmelin, Gustavo Sudre and Punit Shah and has published in prestigious journals such as NeuroImage, Figshare and 2022 International Conference on Robotics and Automation (ICRA).

In The Last Decade

Mark Palatucci

7 papers receiving 558 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.

Zero-Shot Learning with Semantic Output Codes

2018 291 citations Mark Palatucci, Dean Pomerleau et al. Figshare profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Palatucci United States	5	349	216	104	67	54	7	573
Pietro Morerio Italy	17	280 0.8×	552 2.6×	100 1.0×	22 0.3×	32 0.6×	62	849
Dmitry Kit United States	9	192 0.6×	217 1.0×	104 1.0×	32 0.5×	12 0.2×	18	478
Vincent Gripon France	13	327 0.9×	149 0.7×	71 0.7×	18 0.3×	23 0.4×	54	599
Timothée Lesort France	5	313 0.9×	136 0.6×	41 0.4×	10 0.1×	22 0.4×	7	459
Wen-Huang Cheng Taiwan	14	131 0.4×	298 1.4×	32 0.3×	35 0.5×	8 0.1×	51	481
Ayan Kumar Bhunia United Kingdom	17	293 0.8×	787 3.6×	52 0.5×	63 0.9×	21 0.4×	30	970
Aneeshan Sain United Kingdom	16	181 0.5×	566 2.6×	43 0.4×	50 0.7×	10 0.2×	38	688
Kishor M. Bhurchandi India	14	79 0.2×	452 2.1×	54 0.5×	16 0.2×	26 0.5×	52	661
Pere‐Pau Vázquez Spain	15	59 0.2×	633 2.9×	35 0.3×	134 2.0×	14 0.3×	78	884
Yuheng Li United States	6	266 0.8×	502 2.3×	12 0.1×	50 0.7×	28 0.5×	11	772

Countries citing papers authored by Mark Palatucci

Since Specialization

Citations

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

Fields of papers citing papers by Mark Palatucci

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Palatucci

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

7 of 7 papers shown

1.

Palatucci, Mark, Brandyn White, Alex Kuefler, et al.. (2022). Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 8652–8659. 29 indexed citations

2.

Igl, Maximilian, Dae Woo Kim, Alex Kuefler, et al.. (2022). Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. 2022 International Conference on Robotics and Automation (ICRA). 2445–2451. 26 indexed citations

3.

Palatucci, Mark, Dean Pomerleau, Geoffrey E. Hinton, & Tom M. Mitchell. (2018). Zero-Shot Learning with Semantic Output Codes. Figshare. 22. 1410–1418. 291 indexed citations breakdown →

4.

Sudre, Gustavo, Dean Pomerleau, Mark Palatucci, et al.. (2012). Tracking neural coding of perceptual and semantic features of concrete nouns. NeuroImage. 62(1). 451–463. 89 indexed citations

5.

Mitchell, Tom M. & Mark Palatucci. (2011). Thought recognition: predicting and decoding brain activity using the zero-shot learning model. 4 indexed citations

6.

Liu, Han, Mark Palatucci, & Jian Zhang. (2009). Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. 649–656. 130 indexed citations

7.

Palatucci, Mark & J. Andrew Carlson. (2008). On the chance accuracies of large collections of classifiers. 744–751. 4 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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