Marcin Moczulski
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Signal Processing
- Computational Theory and Mathematics
- Co-authors
- Ziyu WangLe SongZichao YangMisha DenilNando de FreitasYoshua BengioÇaǧlar GülçehreJose Sotelo
- Topics
- Stochastic Gradient Optimization Techniques (2 papers)Reinforcement Learning in Robotics (2 papers)Image Retrieval and Classification Techniques (1 paper)
- Journals
- arXiv (Cornell University)International Conference on Learning RepresentationsPomiary Automatyka Kontrola
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
Marcin Moczulski
4 papers receiving 124 citations
Peers
Comparison fields: 5 of 32
- Computer Vision and Pattern Recognition 94
- Artificial Intelligence 81
- Electrical and Electronic Engineering 11
- Signal Processing 8
- Computational Theory and Mathematics 7
Countries citing papers authored by Marcin Moczulski
This map shows the geographic impact of Marcin Moczulski'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 Marcin Moczulski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcin Moczulski more than expected).
Fields of papers citing papers by Marcin Moczulski
This network shows the impact of papers produced by Marcin Moczulski. 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 Marcin Moczulski. The network helps show where Marcin Moczulski may publish in the future.
Co-authorship network of co-authors of Marcin Moczulski
This figure shows the co-authorship network connecting the top 25 collaborators of Marcin Moczulski. A scholar is included among the top collaborators of Marcin Moczulski 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 Marcin Moczulski. Marcin Moczulski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy. | 2 |
| 2 | Contingency-Aware Exploration in Reinforcement Learning | 10 |
| 3 | 11 | |
| 4 | 104 | |
| 5 | Akwizycja i wstępna segmentacja obrazu dla terminali GSM | 0 |
About Marcin Moczulski
Marcin Moczulski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 5 papers that have together received 127 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (2 papers), Reinforcement Learning in Robotics (2 papers) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Computational Mathematics (4 citations), Computer Vision and Pattern Recognition (94 citations) and Artificial Intelligence (81 citations). Marcin Moczulski has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Ziyu Wang, Le Song, Zichao Yang, Misha Denil, Nando de Freitas, Yoshua Bengio, Çaǧlar Gülçehre, Jose Sotelo, Mohammad Norouzi and Jongwook Choi. Their work appears in journals such as arXiv (Cornell University), International Conference on Learning Representations and Pomiary Automatyka Kontrola.
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.