Mikolaj Binkowski

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 382 researchers and is trusted by 194 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 286 citations into it; #5,439 of 35,345 by trust here, #4,923 by citations
• Computer Vision and Pattern Recognition — 224 citations into it; #2,836 of 21,575 by trust here, #3,426 by citations
• Generative Models — 106 citations into it; #327 of 1,956 by trust here, #304 by citations
• Natural Language Processing — 78 citations into it
• Signal Processing — 67 citations into it; #1,300 of 7,572 by trust here, #720 by citations
• Model Architectures — 57 citations into it; #989 of 2,278 by trust here, #2,009 by citations
• Speech & Audio — 40 citations into it; #431 of 1,076 by trust here, #312 by citations
• Optimization & Training — 30 citations into it

Whom Mikolaj Binkowski trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Kaiming He
7. Jimmy Ba
8. Ilya Sutskever
9. Alex Graves
10. L. Eon Bottou

Who trusts Mikolaj Binkowski overall

1. Ali Borji
2. Xu Tan
3. Kwot Sin Lee
4. Anastasia Borovykh
5. Gaurav Parmar
6. Richard Zhang
7. Chenlin Meng
8. Jiatao Gu
9. Tuomas Kynkaanniemi
10. Eric Chan

Papers from Mikolaj Binkowski's perspective

Ordered by how strongly the researchers Mikolaj Binkowski trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Demystifying MMD GANs (2018) — 2,101 citations
• High Fidelity Speech Synthesis with Adversarial Networks (2019) — 264 citations
• End-to-End Adversarial Text-to-Speech (2020) — 197 citations
• Autoregressive Convolutional Neural Networks for Asynchronous Time Series (2017) — 164 citations
• Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations
• Step-unrolled Denoising Autoencoders for Text Generation (2021) — 152 citations