Maxim Berman

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 581 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #4,130 of 21,575 by trust here, #6,426 by citations
• Artificial Intelligence — 71 citations into it; #12,574 of 35,345 by trust here, #11,750 by citations
• Model Architectures — 23 citations into it
• Optimization & Training — 20 citations into it; #561 of 2,601 by trust here, #1,195 by citations
• Radiology, Nuclear Medicine and Imaging — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Aerospace Engineering — 9 citations into it
• Biomedical Engineering — 7 citations into it

Whom Maxim Berman trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Ross B. Girshick
7. Yoshua Bengio
8. Ilya Sutskever
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Maxim Berman overall

1. Hugo Touvron
2. Yauhen Babakhin
3. Shruti Jadon
4. Tom Eelbode
5. Peike Li
6. Ali Athar
7. Linfeng Tang
8. Yuan-Ko Huang
9. Ran Cheng
10. Seyed Hamid Rezatofighi

Papers from Maxim Berman's perspective

Ordered by how strongly the researchers Maxim Berman 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• MultiGrain: a unified image embedding for classes and instances (2019) — 127 citations
• The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks (2017) — 997 citations
• The Lov´asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
• AOWS: Adaptive and Optimal Network Width Search With Latency Constraints (2020) — 30 citations
• Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index (2020) — 353 citations
• Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice (2019) — 345 citations