Igor Babuschkin

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 13 papers; trusts 202 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 287 citations into it; #29,809 of 35,345 by trust here, #462 by citations
• Computer Vision and Pattern Recognition — 151 citations into it
• Natural Language Processing — 105 citations into it
• Reinforcement Learning — 82 citations into it
• Model Architectures — 48 citations into it
• Generative Models — 44 citations into it
• Optimization & Training — 44 citations into it
• Large Language Models — 42 citations into it

Whom Igor Babuschkin trusts overall

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

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

Who trusts Igor Babuschkin overall

1. Xin Eric Wang
2. Takuma Okamoto
3. Wei Ping
4. Kainan Peng
5. Michael R Douglas
6. Lauri Juvela
7. Gilles Louppe
8. Junichi Yamagishi
9. Naihan Li
10. Ryuichi Yamamoto

Papers from Igor Babuschkin's perspective

Ordered by how strongly the researchers Igor Babuschkin 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) — 4,437 citations
• Parallel WaveNet: Fast High-Fidelity Speech Synthesis (2017) — 912 citations
• Competition-level code generation with AlphaCode (2022) — 2,356 citations
• Synthesizing Programs for Images using Reinforced Adversarial Learning (2018) — 243 citations
• Relational Deep Reinforcement Learning (2018) — 245 citations
• Formal Mathematics Statement Curriculum Learning (2022) — 177 citations
• Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2022) — 696 citations
• GPT-4 Technical Report (2023) — 26,133 citations
• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (2022) — 295 citations
• Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (2021) — 177 citations
• Deep reinforcement learning with relational inductive biases (2018) — 231 citations