Roman Bachmann
Computer Vision and Pattern Recognition · 2021–2022
A researcher in the Science corpus: 2 papers; trusts 611 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 118 citations into it
- Artificial Intelligence — 85 citations into it
- Representation & Self-Supervised Learning — 33 citations into it
- Evaluation & Benchmarks — 22 citations into it
- Multimodal Learning — 22 citations into it
- Model Architectures — 17 citations into it
- Aerospace Engineering — 11 citations into it
- Optimization & Training — 10 citations into it
Whom Roman Bachmann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Andrew Zisserman
- Yann LeCun
- Ilya Sutskever
- Karen Simonyan
Who trusts Roman Bachmann overall
Papers from Roman Bachmann's perspective
Ordered by how strongly the researchers Roman Bachmann trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- MultiMAE: Multi-modal Multi-task Masked Autoencoders (2022) — 412 citations
- Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (2021) — 448 citations