Agustinus Kristiadi
Artificial Intelligence · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 89 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 83 citations into it
- Probabilistic & Bayesian Methods — 40 citations into it
- Computer Vision and Pattern Recognition — 29 citations into it
- Optimization & Training — 22 citations into it
- Safety, Robustness & Interpretability — 22 citations into it
- Model Architectures — 10 citations into it
- Statistical and Nonlinear Physics — 6 citations into it
- Computational Mechanics — 5 citations into it
Whom Agustinus Kristiadi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David John Cameron MacKay
- Geoffrey E. Hinton
- Kaiming He
- Radford M. Neal
- Yann LeCun
- Alex Krizhevsky
- Yoshua Bengio
- Diederik P. Kingma
- Alex Graves
- Yarin Gal
Who trusts Agustinus Kristiadi overall
- Laurent Valentin Jospin
- Erik A. Daxberger
- Jeremiah Zhe Liu
- Jakob Gawlikowski
- Michael Matena
- Wray L. Buntine
- Zi Lin
Papers from Agustinus Kristiadi's perspective
Ordered by how strongly the researchers Agustinus Kristiadi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Laplace Redux - Effortless Bayesian Deep Learning (2021) — 467 citations
- Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks (2020) — 365 citations