Christian Tjandraatmadja

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 50 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Computational Theory and Mathematics — 27 citations into it
• Reinforcement Learning — 26 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• Model Architectures — 19 citations into it
• Computer Networks and Communications — 17 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Christian Tjandraatmadja trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Razvan Pascanu
7. Ilya Sutskever
8. Kaiming He
9. Simon S. Haykin
10. L. Eon Bottou

Who trusts Christian Tjandraatmadja overall

1. Matteo Fischetti
2. Moonkyung Ryu
3. Jason Jo
4. Arslan Chaudhry
5. Raman Arora

Papers from Christian Tjandraatmadja's perspective

Ordered by how strongly the researchers Christian Tjandraatmadja 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CAQL: Continuous Action Q-Learning (2019) — 52 citations
• Strong mixed-integer programming formulations for trained neural networks (2018) — 321 citations
• Solving Mixed Integer Programs Using Neural Networks (2020) — 322 citations
• Bounding and Counting Linear Regions of Deep Neural Networks (2017) — 303 citations