Lovekesh Vig

4.5k total citations · 1 hit paper
81 papers, 2.2k citations indexed

About

Lovekesh Vig is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Lovekesh Vig has authored 81 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Artificial Intelligence, 26 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Lovekesh Vig's work include Topic Modeling (19 papers), Natural Language Processing Techniques (13 papers) and Anomaly Detection Techniques and Applications (10 papers). Lovekesh Vig is often cited by papers focused on Topic Modeling (19 papers), Natural Language Processing Techniques (13 papers) and Anomaly Detection Techniques and Applications (10 papers). Lovekesh Vig collaborates with scholars based in India, United States and Malaysia. Lovekesh Vig's co-authors include Gautam Shroff, Puneet Agarwal, Pankaj Malhotra, Sucheta Chauhan, Julie A. Adams, Monika Sharma, Monika Sharma, Shandar Ahmad, D. Garg and Ramya Hebbalaguppe and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Expert Systems with Applications.

In The Last Decade

Lovekesh Vig

72 papers receiving 2.1k citations

Hit Papers

align trajectories sort by overperformance

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.

Long Short Term Memory Networks for Anomaly Detection in Time Series.

2015 748 citations Pankaj Malhotra, Lovekesh Vig et al. The European Symposium on Artificial Neural Networks profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lovekesh Vig India	18	1.1k	611	414	340	232	81	2.2k
Nurhan Karaboğa Türkiye	19	1.5k 1.3×	250 0.4×	435 1.1×	431 1.3×	511 2.2×	56	3.2k
Zaid Abdi Alkareem Alyasseri Iraq	28	948 0.8×	275 0.5×	259 0.6×	288 0.8×	315 1.4×	90	2.4k
Huanlai Xing China	29	1.2k 1.0×	1.4k 2.3×	265 0.6×	536 1.6×	192 0.8×	145	3.2k
Abeer D. Algarni Saudi Arabia	23	629 0.5×	428 0.7×	236 0.6×	523 1.5×	90 0.4×	153	2.2k
Hari Mohan Pandey United Kingdom	31	923 0.8×	583 1.0×	151 0.4×	909 2.7×	174 0.8×	113	3.1k
Siti Mariyam Shamsuddin Malaysia	23	1.2k 1.0×	284 0.5×	172 0.4×	365 1.1×	227 1.0×	133	2.4k
Kuangrong Hao China	29	1.2k 1.1×	753 1.2×	113 0.3×	578 1.7×	611 2.6×	262	3.3k
M. Hassaballah Egypt	27	911 0.8×	169 0.3×	271 0.7×	1.2k 3.5×	150 0.6×	84	2.8k
Anca Ralescu United States	19	729 0.6×	202 0.3×	237 0.6×	384 1.1×	128 0.6×	157	1.7k

Countries citing papers authored by Lovekesh Vig

Since Specialization

Citations

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

Fields of papers citing papers by Lovekesh Vig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lovekesh Vig

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

20 of 20 papers shown

Patwardhan, Manasi, et al.. (2024). Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java Refinement. 46–53. 2 indexed citations

Patwardhan, Manasi, et al.. (2024). An Interactive Co-Pilot for Accelerated Research Ideation. 60–73. 1 indexed citations

Vig, Lovekesh, et al.. (2021). Deep learning based network similarity for model selection. 4(2). 63–83. 1 indexed citations

Srivastava, Saurabh, et al.. (2020). Improved Question Answering using Domain Prediction.. Knowledge Discovery and Data Mining. 2 indexed citations

Sharma, Monika, et al.. (2020). Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning. 3142–3151. 45 indexed citations

Vig, Lovekesh, et al.. (2020). Guided-LIME: Structured Sampling based Hybrid Approach towards Explaining Blackbox Machine Learning Models.. 5 indexed citations

Garg, D., et al.. (2019). NISER: Normalized Item and Session Representations with Graph Neural Networks. arXiv (Cornell University). 20 indexed citations

Sharma, Monika, et al.. (2019). ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks. arXiv (Cornell University). 1–7. 9 indexed citations

Ukil, Arijit, Pankaj Malhotra, Soma Bandyopadhyay, et al.. (2019). Fusing Features based on Signal Properties and TimeNet for Time Series Classification.. The European Symposium on Artificial Neural Networks. 3 indexed citations

10.

Chauhan, Sucheta, Lovekesh Vig, & Shandar Ahmad. (2019). ECG anomaly class identification using LSTM and error profile modeling. Computers in Biology and Medicine. 109. 14–21. 37 indexed citations

11.

Malhotra, Pankaj, et al.. (2018). Using Features From Pre-trained TimeNET For Clinical Predictions.. International Joint Conference on Artificial Intelligence. 38–44. 14 indexed citations

12.

Verma, Ankit & Lovekesh Vig. (2018). ACF Based Feature Extraction and Mixture of Expert CNNs for Pedestrian Detection. 10(4). 90–97. 1 indexed citations

13.

Sharma, Monika, et al.. (2017). Crowdsourcing for Chromosome Segmentation and Deep Classification. 786–793. 58 indexed citations

14.

Malhotra, Pankaj, Vishnu Tv, Lovekesh Vig, Puneet Agarwal, & Gautam Shroff. (2017). TimeNet: Pre-trained deep recurrent neural network for time series classification.. The European Symposium on Artificial Neural Networks. 6 indexed citations

15.

Vig, Lovekesh, et al.. (2017). Improved prediction of missing protein interactome links via anomaly detection. Applied Network Science. 2(1). 2–2. 8 indexed citations

16.

Vig, Lovekesh, et al.. (2016). Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs.. Neural Information Processing Systems. 1 indexed citations

17.

Malhotra, Pankaj, Lovekesh Vig, Gautam Shroff, & Puneet Agarwal. (2015). Long Short Term Memory Networks for Anomaly Detection in Time Series.. The European Symposium on Artificial Neural Networks. 748 indexed citations breakdown →

18.

Agarwal, Manoj, Lovekesh Vig, & Naveen Kumar. (2011). Multiple Objective Robot Coalition Formation. SHILAP Revista de lepidopterología. 20(4). 395–413. 1 indexed citations

19.

Vig, Lovekesh, et al.. (2011). A Neurocomputational Model for the Relation Between Hunger, Dopamine and Action Rate. SHILAP Revista de lepidopterología. 20(4). 373–393. 1 indexed citations

20.

Vig, Lovekesh & Julie A. Adams. (2005). A Framework for Multi-Robot Coalition Formation.. Indian International Conference on Artificial Intelligence. 347–363. 12 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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