Alexander Ilin
Artificial Intelligence · 2010–2020
A researcher in the Science corpus: 9 papers; trusts 230 researchers and is trusted by 159 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 61 citations into it; #10,846 of 35,345 by trust here, #21,316 by citations
- Computer Vision and Pattern Recognition — 59 citations into it; #2,837 of 21,575 by trust here, #8,205 by citations
- Probabilistic & Bayesian Methods — 51 citations into it; #501 of 1,525 by trust here, #648 by citations
- Generative Models — 31 citations into it
- Representation & Self-Supervised Learning — 31 citations into it
- Optimization & Training — 19 citations into it; #564 of 2,601 by trust here, #1,022 by citations
- Signal Processing — 16 citations into it; #7,239 of 7,572 by trust here, #4,560 by citations
- Model Architectures — 14 citations into it
Whom Alexander Ilin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ruslan Salakhutdinov
- Yoshua Bengio
- Radford M. Neal
- Alex Krizhevsky
- Yann LeCun
- Kyunghyun Cho
- Diederik P. Kingma
- Pascal Vincent
- Ilya Sutskever
Who trusts Alexander Ilin overall
- Yoshua Bengio
- Kyunghyun Cho
- Asja Fischer
- Tapani Raiko
- J. Urgen Schmidhuber
- Aaron C. Courville
- Ruslan Salakhutdinov
- Thomas L. Stubbs
- Zhe Gan
- Arto Klami
Papers from Alexander Ilin's perspective
Ordered by how strongly the researchers Alexander Ilin 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
- 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
- Enhanced Gradient for Training Restricted Boltzmann Machines (2013) — 67 citations
- Parallel tempering is efficient for learning restricted Boltzmann machines (2010) — 92 citations
- A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines (2013) — 26 citations
- Recurrent Ladder Networks (2017) — 41 citations
- Conditional Spoken Digit Generation with StyleGAN (2020) — 11 citations
- Tikhonov-Type Regularization for Restricted Boltzmann Machines (2012) — 15 citations
- Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (2011) — 87 citations
- Estimation of ECHAM5 climate model closure parameters with adaptive MCMC (2010) — 61 citations
- Tkk Reports in Information and Computer Science Practical Approaches to Principal Component Analysis in the Presence of Missing Values Tkk Reports in Information and Computer Science Practical Approaches to Principal Component Analysis in the Presence of Missing Values