Clemens Rosenbaum
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 5 papers; trusts 184 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 98 citations into it; #5,142 of 35,345 by trust here, #6,614 by citations
- Reinforcement Learning — 53 citations into it
- Computer Vision and Pattern Recognition — 43 citations into it
- Model Architectures — 34 citations into it; #448 of 2,278 by trust here, #762 by citations
- Representation & Self-Supervised Learning — 22 citations into it
- Cognitive Neuroscience — 15 citations into it
- Optimization & Training — 15 citations into it
- Efficiency & Compression — 10 citations into it
Whom Clemens Rosenbaum trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald J. Williams
- Alex Krizhevsky
- Geoffrey E. Hinton
- Richard S. Sutton
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Quoc V. Le
- Andrew Barto
- Kaiming He
Who trusts Clemens Rosenbaum overall
- Jorge Armando Mendez Mendez
- Matthew Riemer
- Arslan Chaudhry
- Ignacio Cases
- Eric Eaton
- Simon Vandenhende
- Ruihan Yang
- Anirudh Goyal
- Rahul Ramesh
- Marc'Aurelio Ranzato
Papers from Clemens Rosenbaum's perspective
Ordered by how strongly the researchers Clemens Rosenbaum 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (2017) — 277 citations
- Routing Networks and the Challenges of Modular and Compositional Computation (2019) — 93 citations
- Eigenoption Discovery through the Deep Successor Representation (2017) — 172 citations
- On the Role of Weight Sharing During Deep Option Learning (2019) — 22 citations
- Recursive Routing Networks: Learning to Compose Modules for Language Understanding (2019) — 29 citations