Théophane Weber

2.9k citations
21 papers · 340 · h-index 9

Impact in

Papers in

    • Reinforcement Learning in Robotics 8
    • Artificial Intelligence in Games 2
    • Bayesian Modeling and Causal Inference 2
    • Privacy-Preserving Technologies in Data 2
    • Neural dynamics and brain function 3

Théophane Weber

20 papers receiving 331 citations

Peers

Théophane Weber
Comparison fields: 5 of 75
  • Artificial Intelligence 151
  • Computer Vision and Pattern Recognition 95
  • Industrial and Manufacturing Engineering 33
  • Cognitive Neuroscience 46
  • Statistical and Nonlinear Physics 27
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Citations per field
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Citations per year

Countries citing papers authored by Théophane Weber

Since Specialization
Citations

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

Fields of papers citing papers by Théophane Weber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Théophane Weber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Théophane Weber Line = papers co-authored together Théophane Weber links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Visual Interaction Networks: Learning a Physics Simulator from Video
201775
2 201568
3
Imagination-Augmented Agents for Deep Reinforcement Learning
201749
4
Attend, infer, repeat: fast scene understanding with generative models
201630
5 201029
6 200920
7 200912
8
On the role of planning in model-based deep reinforcement learning
202111
9 201310
10
Combining Q-Learning and Search with Amortized Value Estimates
20206
11 20115
12 20155
13 20104
14
Single-Agent Policy Tree Search With Guarantees
20183
15 20103
16 20193
17 20083
18 20212
19
Value-driven Hindsight Modelling
20201
20
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
20181

About Théophane Weber

Théophane Weber is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Networks and Communications, Molecular Biology and Computational Theory and Mathematics, having authored 21 papers that have together received 340 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Neural dynamics and brain function (3 papers), Gene Regulatory Network Analysis (3 papers), Artificial Intelligence in Games (2 papers), Bayesian Modeling and Causal Inference (2 papers), Privacy-Preserving Technologies in Data (2 papers), Complexity and Algorithms in Graphs (2 papers) and Error Correcting Code Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (151 citations), Computer Vision and Pattern Recognition (95 citations), Industrial and Manufacturing Engineering (33 citations), Cognitive Neuroscience (46 citations) and Statistical and Nonlinear Physics (27 citations). Théophane Weber has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Nicolas Heess, Peter Battaglia, John Schulman, Pieter Abbeel, Razvan Pascanu, Jérémie Gallien, Andrea Tacchetti, Nicholas Watters, Daniel Zoran and François Vialatte. Their work appears in journals such as Neural Computation, Mathematics of Operations Research, Proceedings of the London Mathematical Society, Manufacturing & Service Operations Management and IEEE Design and Test.

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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