Atilim Gunes Baydin
Artificial Intelligence · 2015–2023
A researcher in the Science corpus: 8 papers; trusts 350 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 175 citations into it; #12,284 of 35,345 by trust here, #10,065 by citations
- Probabilistic & Bayesian Methods — 97 citations into it; #897 of 1,525 by trust here, #751 by citations
- Computer Vision and Pattern Recognition — 81 citations into it; #19,681 of 21,575 by trust here, #11,498 by citations
- Optimization & Training — 56 citations into it; #632 of 2,601 by trust here, #525 by citations
- Statistics and Probability — 32 citations into it
- Natural Language Processing — 20 citations into it
- Nuclear and High Energy Physics — 19 citations into it
- Model Architectures — 18 citations into it
Whom Atilim Gunes Baydin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- L. Eon Bottou
- Yoshua Bengio
- Geoffrey E. Hinton
- Radford M. Neal
- Alex Krizhevsky
- Yann LeCun
- Max Welling
- J. Urgen Schmidhuber
- Ronald J. Williams
Who trusts Atilim Gunes Baydin overall
- Maizar Raissi
- G. Em Karniadakis
- Paris Perdikaris
- Ameya Dilip Jagtap
- Yoav Goldberg
- Sifan Wang
- Iain Murray
- Dougal Maclaurin
- Charles C. Margossian
- Yibo Yang
Papers from Atilim Gunes Baydin's perspective
Ordered by how strongly the researchers Atilim Gunes Baydin 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
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Online Learning Rate Adaptation with Hypergradient Descent (2017) — 284 citations
- Managing extreme AI risks amid rapid progress (2023) — 431 citations
- Inference Compilation and Universal Probabilistic Programming (2016) — 149 citations
- Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018) — 33 citations
- Alpha MAML: Adaptive Model-Agnostic Meta-Learning (2019) — 72 citations
- Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019) — 63 citations
- Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019) — 15 citations
- Automatic differentiation in machine learning: a survey (2015) — 3,658 citations