Steffen Schneider
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 8 papers; trusts 454 researchers and is trusted by 84 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 192 citations into it; #3,565 of 35,345 by trust here, #6,422 by citations
- Computer Vision and Pattern Recognition — 131 citations into it
- Representation & Self-Supervised Learning — 78 citations into it; #2,534 of 3,145 by trust here, #1,811 by citations
- Signal Processing — 53 citations into it; #474 of 7,572 by trust here, #794 by citations
- Speech & Audio — 51 citations into it; #88 of 1,076 by trust here, #197 by citations
- Natural Language Processing — 48 citations into it; #956 of 5,688 by trust here, #1,779 by citations
- Model Architectures — 24 citations into it
- Optimization & Training — 21 citations into it
Whom Steffen Schneider trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Yoshua Bengio
- Diederik P. Kingma
- Ronan Collobert
- Ilya Sutskever
- Christian Szegedy
- Yann LeCun
- Karen Simonyan
Who trusts Steffen Schneider overall
- Alexei Baevski
- Yu-An Chung
- Wei-Ning Hsu
- Shaoshi Ling
- Abdel-rahman Mohamed
- Jinyu Li
- Andy T. Liu
- Michael Auli
- Hung-yi Lee
- Marvin Zhang
Papers from Steffen Schneider's perspective
Ordered by how strongly the researchers Steffen Schneider trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Improving robustness against common corruptions by covariate shift adaptation (2020) — 651 citations
- vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations (2019) — 744 citations
- Contrastive Learning Inverts the Data Generating Process (2021) — 283 citations
- Multi-animal pose estimation, identification and tracking with DeepLabCut (2022) — 506 citations
- Learnable latent embeddings for joint behavioural and neural analysis (2022) — 412 citations
- Speech Recognition (2019) — 717 citations
- wav2vec: Unsupervised Pre-training for Speech Recognition (2019)
- Speech Recognition (2018)