Daniel Kappler

Computer Vision and Pattern Recognition · 2011–2022

A researcher in the Science corpus: 8 papers; trusts 155 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it
• Artificial Intelligence — 93 citations into it; #8,021 of 35,345 by trust here, #7,945 by citations
• Control and Systems Engineering — 73 citations into it; #1,056 of 8,257 by trust here, #536 by citations
• Reinforcement Learning — 62 citations into it; #2,170 of 2,242 by trust here, #1,092 by citations
• Robotics & Control — 38 citations into it
• Aerospace Engineering — 31 citations into it
• Biomedical Engineering — 24 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Daniel Kappler trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Sergey Levine
5. Ronan Collobert
6. Diederik P. Kingma
7. Jia Deng
8. Rosen Diankov
9. Kaiming He
10. Yann LeCun

Who trusts Daniel Kappler overall

1. Sergey Levine
2. Jeffrey Mahler
3. Lerrel Pinto
4. Andreas ten Pas
5. Avi Singh
6. Marcus Gualtieri
7. Abhinav Kumar Gupta
8. Peter Pastor
9. Stephen James
10. Deirdre Quillen

Papers from Daniel Kappler's perspective

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

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

Their papers

• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022) — 813 citations
• Open-vocabulary Queryable Scene Representations for Real World Planning (2022) — 257 citations
• Leveraging big data for grasp planning (2015) — 307 citations
• Real-Time Perception Meets Reactive Motion Generation (2017) — 124 citations
• Watch, Try, Learn: Meta-Learning from Demonstrations and Reward (2019) — 54 citations
• Robot arm pose estimation by pixel-wise regression of joint angles (2016) — 43 citations
• Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data (2020) — 26 citations
• The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation (2011) — 55 citations