Iuri Frosio

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 282 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #32,352 of 35,345 by trust here, #10,942 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #9,756 of 21,575 by trust here, #5,422 by citations
• Reinforcement Learning — 54 citations into it; #1,771 of 2,242 by trust here, #1,482 by citations
• Media Technology — 19 citations into it; #1,930 of 3,621 by trust here, #839 by citations
• Evaluation & Benchmarks — 14 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 10 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Iuri Frosio trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Zhou Wang
5. Yann LeCun
6. Volodymyr Mnih
7. Diederik P. Kingma
8. Ilya Sutskever
9. Richard S. Sutton
10. Yoshua Bengio

Who trusts Iuri Frosio overall

1. Steven Dalton
2. Yuxi Li
3. Chongyi Li
4. Igor Adamski
5. Yotam Nitzan
6. Adam Stooke
7. Brennan Shacklett
8. Michael Everett
9. Alfredo V. Clemente
10. Yuandong Tian

Papers from Iuri Frosio's perspective

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

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

Their papers

• Loss Functions for Image Restoration With Neural Networks (2017) — 2,469 citations
• Importance Estimation for Neural Network Pruning (2019) — 1,174 citations
• Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU (2016) — 297 citations
• How to Close Sim-Real Gap? Transfer with Segmentation! (2020) — 10 citations
• Loss Functions for Neural Networks for Image Processing (2015) — 314 citations
• Accelerating Reinforcement Learning through GPU Atari Emulation (2019) — 48 citations
• GA3C: GPU-based A3C for Deep Reinforcement Learning (2016) — 75 citations