Roland Vollgraf

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 417 researchers and is trusted by 620 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #1,094 of 35,345 by trust here, #1,834 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #1,900 of 21,575 by trust here, #1,448 by citations
• Natural Language Processing — 49 citations into it; #1,011 of 5,688 by trust here, #1,735 by citations
• Generative Models — 39 citations into it; #1,377 of 1,956 by trust here, #1,336 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Model Architectures — 20 citations into it
• Signal Processing — 16 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it

Whom Roland Vollgraf trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Alex Krizhevsky
5. Yoshua Bengio
6. Alex Graves
7. Yann LeCun
8. Sepp Hochreiter
9. J. Urgen Schmidhuber
10. Kaiming He

Who trusts Roland Vollgraf overall

1. Fei Ye
2. Jinlan Fu
3. Alexander Rainer Tassilo Gepperth
4. Yang Song
5. Adrian G. Bors
6. Jaehong Yoon
7. Xinyu Wang
8. Timothée Lesort
9. Benedikt Pfülb
10. Stanislaw Jastrzebski

Papers from Roland Vollgraf's perspective

Ordered by how strongly the researchers Roland Vollgraf 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms (2017) — 10,881 citations
• Learning Texture Manifolds with the Periodic Spatial GAN (2017) — 179 citations
• FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (2019) — 933 citations
• Texture Synthesis with Spatial Generative Adversarial Networks (2016) — 226 citations
• Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting (2021) — 554 citations
• Pooled Contextualized Embeddings for Named Entity Recognition (2019) — 273 citations
• Contextual String Embeddings for Sequence Labeling (2018) — 1,382 citations