Martin Wöllmer
Artificial Intelligence · 2008–2013
A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 76 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 22 citations into it; #7,004 of 35,345 by trust here, #8,619 by citations
- Model Architectures — 19 citations into it
- Signal Processing — 17 citations into it; #337 of 7,572 by trust here, #538 by citations
- Natural Language Processing — 13 citations into it
- Experimental and Cognitive Psychology — 12 citations into it; #1,556 of 9,731 by trust here, #3,963 by citations
- Computer Vision and Pattern Recognition — 5 citations into it; #4,238 of 21,575 by trust here, #5,890 by citations
Whom Martin Wöllmer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Graves
- Sepp Hochreiter
- J. Urgen Schmidhuber
- Björn Schuller
- Florian Eyben
- Paul P. G. Boersma
- Mark A. Hall
- Santiago Fernández
- Chih-Jen Lin
- Yoshua Bengio
Who trusts Martin Wöllmer overall
- Björn Schuller
- Soujanya Poria
- Florian Eyben
- Amir Zadeh
- Erik Cambria
- Michel François Valstar
- Devamanyu Hazarika
- J. Urgen Schmidhuber
- Yonatan Belinkov
- Louis-philippe Morency
Papers from Martin Wöllmer's perspective
Ordered by how strongly the researchers Martin Wöllmer trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Opensmile: the munich versatile and fast open-source audio feature extractor (2010) — 3,249 citations
- From speech to letters - using a novel neural network architecture for grapheme based ASR (2009) — 52 citations
- YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context (2013) — 414 citations
- OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit (2009) — 453 citations
- Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies (2008) — 317 citations
- Keyword spotting exploiting Long Short-Term Memory (2013) — 56 citations
- Online Driver Distraction Detection Using Long Short-Term Memory (2011) — 169 citations