Krzysztof J. Geras

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 10 papers; trusts 175 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it
• Computer Vision and Pattern Recognition — 89 citations into it
• Model Architectures — 44 citations into it
• Optimization & Training — 44 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Signal Processing — 21 citations into it
• Computational Mechanics — 16 citations into it
• Natural Language Processing — 13 citations into it

Whom Krzysztof J. Geras trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Alex Graves
5. Yann LeCun
6. Pascal Vincent
7. Diederik P. Kingma
8. Xavier Glorot
9. Ilya Sutskever
10. Kaiming He

Who trusts Krzysztof J. Geras overall

1. Zengyi Li
2. Qianjun Zhang
3. Arash Vahdat
4. Yubei Chen
5. Mary Phuong
6. Friedrich T. Sommer
7. Gregor Urban
8. Lei Zhang
9. Seonguk Park
10. Zheng Xu

Papers from Krzysztof J. Geras's perspective

Ordered by how strongly the researchers Krzysztof J. Geras 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. 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

• The Break-Even Point on Optimization Trajectories of Deep Neural Networks (2020) — 203 citations
• Blending LSTMs into CNNs (2015) — 61 citations
• Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019) — 596 citations
• High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks (2017) — 245 citations
• fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2020) — 514 citations
• Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018) — 1,072 citations
• Analysis of Deep Neural Networks with Extended Data Jacobian Matrix (2016) — 38 citations
• Do Deep Convolutional Nets Really Need to be Deep and Convolutional? (2016) — 259 citations
• Scheduled denoising autoencoders (2014) — 49 citations