Reinhard Heckel

Computer Vision and Pattern Recognition · 2015–2024

A researcher in the Science corpus: 9 papers; trusts 253 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #7,670 of 21,575 by trust here, #13,269 by citations
• Artificial Intelligence — 56 citations into it; #19,175 of 35,345 by trust here, #34,943 by citations
• Computational Mechanics — 27 citations into it; #5,337 of 10,001 by trust here, #5,350 by citations
• Generative Models — 23 citations into it
• Molecular Biology — 20 citations into it
• Optimization & Training — 18 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Statistical Learning Theory — 15 citations into it

Whom Reinhard Heckel trusts overall

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

1. Samet Oymak
2. Simon Shaolei Du
3. Anthony V. Robins
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Sergey Ioffe
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Kaiming He

Who trusts Reinhard Heckel overall

1. Liangzu Peng
2. Yunpeng Chen
3. Chun-Hua Tsai
4. Georgina Peake
5. Ajil Jalal
6. Paris V. Giampouras
7. Fei Wang
8. René Vidal
9. Qi Lei
10. Chen Gao

Papers from Reinhard Heckel's perspective

Ordered by how strongly the researchers Reinhard Heckel 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation (2020) — 93 citations
• A Provably Convergent Scheme for Compressive Sensing Under Random Generative Priors (2018) — 33 citations
• DiffuserCam: Lensless Single-exposure 3D Imaging (2017) — 500 citations
• Robust chemical preservation of digital information on DNA in silica with error-correcting codes. (2015) — 707 citations
• Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016) — 59 citations
• DataComp-LM: In search of the next generation of training sets for language models (2024) — 374 citations
• Provable Continual Learning via Sketched Jacobian Approximations (2021) — 14 citations
• A Characterization of the DNA Data Storage Channel (2018) — 318 citations
• Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks (2018) — 339 citations