Gustav Eje Henter

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 7 papers; trusts 177 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #15,530 of 35,345 by trust here, #15,236 by citations
• Signal Processing — 56 citations into it; #4,117 of 7,572 by trust here, #2,457 by citations
• Speech & Audio — 39 citations into it
• Natural Language Processing — 36 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #15,210 of 21,575 by trust here, #21,424 by citations
• Generative Models — 25 citations into it
• Model Architectures — 14 citations into it; #1,638 of 2,278 by trust here, #2,192 by citations
• Large Language Models — 9 citations into it

Whom Gustav Eje Henter trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Graves
5. Alex Krizhevsky
6. Radford M. Neal
7. Aäron van den Oord
8. Ilya Sutskever
9. Quoc V. Le
10. Yann LeCun

Who trusts Gustav Eje Henter overall

1. Wei-Ning Hsu
2. Yi Zhao
3. Vincent Wan
4. Eric Battenberg
5. Guanghui Xu
6. Yu Zhang
7. Yukiya Hono
8. Junichi Yamagishi
9. Ofir Press
10. Xin Eric Wang

Papers from Gustav Eje Henter's perspective

Ordered by how strongly the researchers Gustav Eje Henter 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models (2021) — 26 citations
• Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis (2018) — 61 citations
• Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis (2018) — 93 citations
• Adapting and controlling DNN-based speech synthesis using input codes (2017) — 77 citations
• A Template-Based Approach for Speech Synthesis Intonation Generation Using LSTMs (2016) — 19 citations
• Median-based generation of synthetic speech durations using a non-parametric approach (2016) — 19 citations
• INTERSPEECH 2014 15th Annual Conference of the International Speech Communication Association (2014)