Martin Takáč

5.4k total citations · 1 hit paper
59 papers, 1.7k citations indexed

About

Martin Takáč is a scholar working on Artificial Intelligence, Computational Mechanics and Cognitive Neuroscience. According to data from OpenAlex, Martin Takáč has authored 59 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 21 papers in Computational Mechanics and 9 papers in Cognitive Neuroscience. Recurrent topics in Martin Takáč's work include Stochastic Gradient Optimization Techniques (23 papers), Sparse and Compressive Sensing Techniques (21 papers) and Child and Animal Learning Development (7 papers). Martin Takáč is often cited by papers focused on Stochastic Gradient Optimization Techniques (23 papers), Sparse and Compressive Sensing Techniques (21 papers) and Child and Animal Learning Development (7 papers). Martin Takáč collaborates with scholars based in United States, United Arab Emirates and Slovakia. Martin Takáč's co-authors include Peter Richtárik, Shamim N. Pakzad, Jakub Konečný, Martin Jaggi, Michael I. Jordan, Virginia Smith, Jie Liu, Soheil Sadeghi Eshkevari, Chenxin Ma and Mohammadreza Nazari and has published in prestigious journals such as SHILAP Revista de lepidopterología, Acta Materialia and The Journal of Physical Chemistry C.

In The Last Decade

Martin Takáč

51 papers receiving 1.6k 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.

Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function

2012 345 citations Peter Richtárik, Martin Takáč profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Martin Takáč United States	17	895	528	241	233	160	59	1.7k
Zaer S. Abo‐Hammour Jordan	16	414 0.5×	103 0.2×	63 0.3×	136 0.6×	162 1.0×	35	1.5k
Yi Zhou China	15	570 0.6×	167 0.3×	44 0.2×	153 0.7×	77 0.5×	99	1.3k
Gang Hu China	19	605 0.7×	328 0.6×	43 0.2×	126 0.5×	162 1.0×	130	1.5k
Vincent Wertz Belgium	21	693 0.8×	66 0.1×	79 0.3×	95 0.4×	148 0.9×	122	2.3k
Yuanyuan Liu China	22	333 0.4×	497 0.9×	40 0.2×	49 0.2×	137 0.9×	85	1.3k
Yoshinobu Kawahara Japan	19	338 0.4×	131 0.2×	47 0.2×	67 0.3×	74 0.5×	85	1.1k
Feng Yin China	25	703 0.8×	131 0.2×	74 0.3×	432 1.9×	898 5.6×	173	2.0k
Usman A. Khan United States	26	1.0k 1.1×	369 0.7×	38 0.2×	2.0k 8.7×	887 5.5×	126	2.9k
Hangjun Che China	23	585 0.7×	177 0.3×	33 0.1×	137 0.6×	121 0.8×	73	1.3k
SingerYoram	6	1.2k 1.3×	167 0.3×	28 0.1×	113 0.5×	94 0.6×	8	1.9k

Countries citing papers authored by Martin Takáč

Since Specialization

Citations

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

Fields of papers citing papers by Martin Takáč

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Takáč

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

Paudyal, Durga, et al.. (2025). Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening. Acta Materialia. 297. 121347–121347.

Takáč, Martin, et al.. (2024). Stochastic Gradient Descent with Pre-Conditioned Polyak Step-Size. Журнал вычислительной математики и математической физики. 64(4). 575–586. 1 indexed citations

Takáč, Martin, et al.. (2024). MagBERT: Magnetics Knowledge Aware Language Model Coupled with a Question Answering Pipeline for Curie Temperature Extraction Task. The Journal of Physical Chemistry C. 128(31). 13217–13229. 2 indexed citations

Song, Kun, et al.. (2024). Robustly Train Normalizing Flows via KL Divergence Regularization. Proceedings of the AAAI Conference on Artificial Intelligence. 38(13). 15047–15055. 1 indexed citations

Takáč, Martin, et al.. (2023). On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling. 5350–5358. 2 indexed citations

Takáč, Martin, et al.. (2023). Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands. IEEE Access. 11. 87958–87969. 6 indexed citations

Sagar, Mark, et al.. (2022). Deconstructing and reconstructing turn‐taking in caregiver‐infant interactions: a platform for embodied models of early cooperation. Journal of the Royal Society of New Zealand. 53(1). 148–168. 5 indexed citations

Rangarajan, Srinivas, et al.. (2022). A deep neural network for oxidative coupling of methane trained on high-throughput experimental data. Journal of Physics Energy. 5(1). 14009–14009. 4 indexed citations

Dvinskikh, Darina, et al.. (2022). Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. SHILAP Revista de lepidopterología. 10. 100041–100041. 4 indexed citations

10.

Knott, Alistair & Martin Takáč. (2020). Roles for Event Representations in Sensorimotor Experience, Memory Formation, and Language Processing. Topics in Cognitive Science. 13(1). 187–205. 11 indexed citations

11.

Eshkevari, Soheil Sadeghi, Shamim N. Pakzad, Martin Takáč, & Thomas J. Matarazzo. (2020). Modal Identification of Bridges Using Mobile Sensors with Sparse Vibration Data. Journal of Engineering Mechanics. 146(4). 59 indexed citations

12.

Nguyen, Lam M., Phuong Ha Nguyen, Marten van Dijk, et al.. (2018). SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 3750–3758. 14 indexed citations

13.

Smith, Virginia, et al.. (2018). CoCoA: A General Framework for Communication-Efficient Distributed Optimization. Journal of Machine Learning Research. 18(230). 1–49. 80 indexed citations

14.

He, Xi, Dheevatsa Mudigere, Mikhail Smelyanskiy, & Martin Takáč. (2017). Distributed Hessian-Free Optimization for Deep Neural Network.. National Conference on Artificial Intelligence. 2 indexed citations

15.

Nazari, Mohammadreza, et al.. (2017). A Deep Q-Network for the Beer Game with Partial Information.. arXiv (Cornell University). 6 indexed citations

16.

Takáč, Martin & Alistair Knott. (2016). Working memory encoding of events and their participants: a neural network model with applications in sensorimotor processing and sentence generation.. Cognitive Science. 3 indexed citations

17.

Takáč, Martin & Alistair Knott. (2016). Mechanisms for storing and accessing event representations in episodic memory, and their expression in language: a neural network model.. Cognitive Science. 3 indexed citations

18.

Takáč, Martin, et al.. (2013). Mini-Batch Primal and Dual Methods for SVMs. International Conference on Machine Learning. 1022–1030. 31 indexed citations

19.

Takáč, Martin & Alistair Knott. (2013). A neural network model of working memory for episodes. Cognitive Science. 35(35). 1 indexed citations

20.

Takáč, Martin, Ľubica Beňušková, & Alistair Knott. (2012). Mapping sensorimotor sequences to word sequences: A connectionist model of language acquisition and sentence generation. Cognition. 125(2). 288–308. 19 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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