F. Meier

Computer Vision and Pattern Recognition · 2011–2025

A researcher in the Science corpus: 15 papers; trusts 477 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 318 citations into it; #10,958 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 307 citations into it; #19,504 of 35,345 by trust here, #6,422 by citations
• Reinforcement Learning — 149 citations into it
• Control and Systems Engineering — 100 citations into it
• Robotics & Control — 82 citations into it
• Aerospace Engineering — 69 citations into it
• Representation & Self-Supervised Learning — 61 citations into it
• Optimization & Training — 34 citations into it

Whom F. Meier trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Radford M. Neal
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Kaiming He
8. Chelsea Finn
9. Sergey Levine
10. Ilya Sutskever

Who trusts F. Meier overall

1. Gobinda Saha
2. Tanmay Shankar
3. Harish Chaandar Ravichandar
4. Muhammad Asif Rana
5. James Smith
6. Zifeng Wang
7. Fei Ye
8. Kaushik Roy
9. Sayna Ebrahimi
10. Julio Hurtado

Papers from F. Meier's perspective

Ordered by how strongly the researchers F. Meier trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control (2017) — 43 citations
• Meta Learning via Learned Loss (2019) — 126 citations
• Adversarial Continual Learning (2020) — 236 citations
• Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? (2023) — 303 citations
• Real-Time Perception Meets Reactive Motion Generation (2017) — 124 citations
• Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021) — 769 citations
• Generalized Inner Loop Meta-Learning (2019) — 169 citations
• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control (2018) — 37 citations
• OpenEQA: Embodied Question Answering in the Era of Foundation Models (2024) — 348 citations
• Movement segmentation using a primitive library (2011) — 94 citations
• Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass (2025) — 356 citations
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025) — 527 citations
• Block Contextual MDPs for Continual Learning (2021) — 34 citations
• On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward (2020) — 171 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations