Miljan Martic
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 7 papers; trusts 128 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 232 citations into it; #5,964 of 35,345 by trust here, #2,245 by citations
- Reinforcement Learning — 192 citations into it; #677 of 2,242 by trust here, #246 by citations
- Safety, Robustness & Interpretability — 101 citations into it; #1,103 of 2,251 by trust here, #1,023 by citations
- Computational Theory and Mathematics — 37 citations into it
- Control and Systems Engineering — 34 citations into it; #2,167 of 8,257 by trust here, #93 by citations
- Management Science and Operations Research — 34 citations into it
- Computer Vision and Pattern Recognition — 30 citations into it
- Cognitive Neuroscience — 21 citations into it
Whom Miljan Martic trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Richard S. Sutton
- Pieter Abbeel
- Geoffrey E. Hinton
- Andrew Y. Ng
- Ronald J. Williams
- Yoshua Bengio
- Alex Graves
- Alex Krizhevsky
- Yann LeCun
Who trusts Miljan Martic overall
- Tom Everitt
- Victoria Krakovna
- Jan Leike
- Simon Zhuang
- Richard Ngo
- Laurent Orseau
- David Krueger
- Daniel S. Brown
- Marcus Hutter
- Iason Gabriel
Papers from Miljan Martic's perspective
Ordered by how strongly the researchers Miljan Martic trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Deep Reinforcement Learning from Human Preferences (2017) — 5,646 citations
- Scalable agent alignment via reward modeling: a research direction (2018) — 615 citations
- AI Safety Gridworlds (2017) — 298 citations
- Causal Analysis of Agent Behavior for AI Safety (2021) — 8 citations
- Avoiding Side Effects By Considering Future Tasks (2020) — 51 citations
- Penalizing Side Effects using Stepwise Relative Reachability (2018) — 64 citations
- Measuring and avoiding side effects using relative reachability (2018) — 24 citations