Gerhard Widmer

Artificial Intelligence · 1993–2021

A researcher in the Science corpus: 7 papers; trusts 115 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #17,030 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #15,836 of 21,575 by trust here, #15,955 by citations
• Signal Processing — 23 citations into it; #5,427 of 7,572 by trust here, #3,428 by citations
• Model Architectures — 19 citations into it
• Optimization & Training — 12 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Generative Models — 7 citations into it; #1,599 of 1,956 by trust here, #1,843 by citations
• Representation & Self-Supervised Learning — 7 citations into it; #2,613 of 3,145 by trust here, #2,957 by citations

Whom Gerhard Widmer trusts overall

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

1. Ryszard S. Michalski
2. David William Aha
3. Sean M. Polyn
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Jeffrey C. Schlimmer
7. Yoshua Bengio
8. Karen Simonyan
9. Steven L. Salzberg
10. Stephen Grossberg

Who trusts Gerhard Widmer overall

1. Indrė Žliobaitė
2. João Gama
3. Ludmila I. Kuncheva
4. Andrea Dal Pozzolo
5. J. Kolter
6. Yehuda Koren
7. Miroslav Kubat
8. Albert Bifet
9. Alexey Tsymbal
10. Ning Lu

Papers from Gerhard Widmer's perspective

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

Their papers

• Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints (2016) — 73 citations
• Efficient Training of Audio Transformers with Patchout (2021) — 410 citations
• Training general-purpose audio tagging networks with noisy labels and iterative self-verification (2018) — 28 citations
• madmom: A New Python Audio and Music Signal Processing Library (2016) — 317 citations
• Learning in the presence of concept drift and hidden contexts (2004) — 407 citations
• Learning in the Presence of Concept Drift and Hidden Contexts (1996)
• Effective Learning in Dynamic Environments by Explicit Context Tracking (1993)