Hannes Schulz

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 11 papers; trusts 408 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it; #7,071 of 35,345 by trust here, #4,682 by citations
• Natural Language Processing — 110 citations into it; #1,858 of 5,688 by trust here, #1,305 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #8,629 of 21,575 by trust here, #7,852 by citations
• Reinforcement Learning — 30 citations into it
• Evaluation & Benchmarks — 26 citations into it; #2,167 of 3,147 by trust here, #1,764 by citations
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 9 citations into it

Whom Hannes Schulz trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alex Graves
8. Kaiming He
9. Jimmy Ba
10. Richard S. Sutton

Who trusts Hannes Schulz overall

1. Bo Li
2. Oliver Lemon
3. Zachary Chase Lipton
4. Paweł Budzianowski
5. Jekaterina Novikova
6. Jose L. Part
7. Baolin Peng
8. Jianfeng Gao
9. Ondrej Dusek
10. Prithviraj Ammanabrolu

Papers from Hannes Schulz's perspective

Ordered by how strongly the researchers Hannes Schulz 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Frames: a corpus for adding memory to goal-oriented dialogue systems (2017) — 280 citations
• RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features (2015) — 346 citations
• Policy Networks with Two-Stage Training for Dialogue Systems (2016) — 110 citations
• Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation (2017) — 232 citations
• Towards Deep Conversational Recommendations (2018) — 483 citations
• The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (2018) — 58 citations
• Recurrent convolutional neural networks for object-class segmentation of RGB-D video (2015) — 23 citations
• Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data (2016) — 31 citations
• Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer (2020) — 18 citations
• Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction (2018) — 129 citations
• In Situ Root System Architecture Extraction from Magnetic Resonance Imaging for Water Uptake Modeling (2013) — 53 citations