Erik Wijmans
Computer Vision and Pattern Recognition · 12 papers · 2019–2021
A researcher in the Science corpus: 12 papers; trusts 763 researchers and is trusted by 134 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 246 citations into it; #2,972 of 21,575 by trust here, #2,016 by citations
- Artificial Intelligence — 201 citations into it; #14,819 of 35,345 by trust here, #4,353 by citations
- Reinforcement Learning — 136 citations into it; #976 of 2,242 by trust here, #414 by citations
- Aerospace Engineering — 90 citations into it
- Robotics & Control — 60 citations into it; #221 of 609 by trust here, #130 by citations
- Optimization & Training — 44 citations into it
- Evaluation & Benchmarks — 41 citations into it
- Natural Language Processing — 41 citations into it
Whom Erik Wijmans trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Ross B. Girshick
- Jimmy Ba
- Yann LeCun
- John Schulman
- Ilya Sutskever
Who trusts Erik Wijmans overall
- Dhruv Batra
- Santhosh K. Ramakrishnan
- Yilun Du
- Luca Weihs
- S. Gadre
- Matt Deitke
- Naoki Yokoyama
- Jiafei Duan
- Tushar Nagarajan
- Kristen Grauman
Papers from Erik Wijmans's perspective
Ordered by how strongly the researchers Erik Wijmans trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Habitat: A Platform for Embodied AI Research (2019) — 2,003 citations
- The Replica Dataset: A Digital Replica of Indoor Spaces (2019) — 1,356 citations
- DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames (2019) — 650 citations
- ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects (2020) — 371 citations
- Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments (2020) — 578 citations
- Embodied Question Answering in Photorealistic Environments With Point Cloud Perception (2019) — 205 citations
- Large Batch Simulation for Deep Reinforcement Learning (2021) — 29 citations
- Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation (2020) — 66 citations
- Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? (2019) — 263 citations
- How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget (2020) — 10 citations
- Auxiliary Tasks Speed Up Learning PointGoal Navigation (2020) — 87 citations
- Are We Making Real Progress in Simulated Environments? Measuring the Sim2Real Gap in Embodied Visual Navigation (2019) — 56 citations