Jakub Sygnowski

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 8 papers; trusts 169 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 245 citations into it; #13,664 of 35,345 by trust here, #3,842 by citations
• Reinforcement Learning — 124 citations into it
• Computer Vision and Pattern Recognition — 99 citations into it; #8,687 of 21,575 by trust here, #3,445 by citations
• Representation & Self-Supervised Learning — 47 citations into it
• Optimization & Training — 39 citations into it
• Meta & Continual Learning — 30 citations into it; #481 of 1,041 by trust here, #89 by citations
• Management Science and Operations Research — 23 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it

Whom Jakub Sygnowski trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Kaiming He
8. Chelsea Finn
9. Oriol Vinyals
10. Ilya Sutskever

Who trusts Jakub Sygnowski overall

1. Han-Jia Ye
2. Johannes von Oswald
3. Wei-Lun Chao
4. Massimo Caccia
5. Tianhe Yu
6. Andrew Kyle Lampinen
7. De-chuan Zhan
8. Hexiang Hu
9. Ji Zhang
10. Timothée Lesort

Papers from Jakub Sygnowski's perspective

Ordered by how strongly the researchers Jakub Sygnowski 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Meta-Learning with Latent Embedding Optimization (2018) — 1,522 citations
• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• Task Agnostic Continual Learning via Meta Learning (2019) — 103 citations
• Regularized Behavior Value Estimation (2021) — 43 citations
• Meta-learning by the Baldwin effect (2018) — 42 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Human-Timescale Adaptation in an Open-Ended Task Space (2023) — 163 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations