Tim Klinger

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 11 papers; trusts 373 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #4,201 of 35,345 by trust here, #3,362 by citations
• Natural Language Processing — 127 citations into it
• Computer Vision and Pattern Recognition — 113 citations into it; #14,500 of 21,575 by trust here, #3,982 by citations
• Model Architectures — 50 citations into it; #437 of 2,278 by trust here, #762 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Reinforcement Learning — 35 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Optimization & Training — 26 citations into it

Whom Tim Klinger trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ronald J. Williams
6. Yann LeCun
7. Alex Graves
8. Ilya Sutskever
9. Kaiming He
10. J. Urgen Schmidhuber

Who trusts Tim Klinger overall

1. Matthew Riemer
2. Jorge Armando Mendez Mendez
3. Ignacio Cases
4. Arslan Chaudhry
5. Clemens Rosenbaum
6. Eric Eaton
7. Simon Vandenhende
8. Lucas Caccia
9. Ying Xu
10. Rahul Ramesh

Papers from Tim Klinger's perspective

Ordered by how strongly the researchers Tim Klinger 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (2017) — 277 citations
• Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering (2017) — 166 citations
• R3: Reinforced Ranker-Reader for Open-Domain Question Answering (2018) — 200 citations
• Routing Networks and the Challenges of Modular and Compositional Computation (2019) — 93 citations
• Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation (2016) — 193 citations
• Scalable Recollections for Continual Lifelong Learning (2017) — 76 citations
• Recursive Routing Networks: Learning to Compose Modules for Language Understanding (2019) — 29 citations
• Logical Rule Induction and Theory Learning Using Neural Theorem Proving (2018) — 45 citations
• Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning (2018) — 5 citations
• Generative Knowledge Distillation for General Purpose Function Compression (2017)
• R3: Reinforced Reader-Ranker for Open-Domain Question Answering (2017) — 64 citations