Berrak Sisman
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 9 papers; trusts 214 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 235 citations into it; #9,847 of 35,345 by trust here, #9,968 by citations
- Signal Processing — 171 citations into it; #2,369 of 7,572 by trust here, #1,485 by citations
- Speech & Audio — 117 citations into it; #701 of 1,076 by trust here, #667 by citations
- Computer Vision and Pattern Recognition — 68 citations into it
- Generative Models — 65 citations into it; #1,838 of 1,956 by trust here, #1,796 by citations
- Natural Language Processing — 50 citations into it
- Experimental and Cognitive Psychology — 27 citations into it
- Model Architectures — 20 citations into it
Whom Berrak Sisman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Alex Graves
- Ilya Sutskever
- Aäron van den Oord
- Oriol Vinyals
- Junichi Yamagishi
- Yann LeCun
Who trusts Berrak Sisman overall
- Rui Liu
- Xu Tan
- Tao Qin
- Haizhou Li
- Andros Tjandra
- Frank K. Soong
- Zhaoxi Mu
- Mingyang Zhang
- Yuma Koizumi
- Kun Zhou
Papers from Berrak Sisman's perspective
Ordered by how strongly the researchers Berrak Sisman trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019 (2019) — 76 citations
- An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020) — 426 citations
- Emotional Voice Conversion: Theory, Databases and ESD (2021) — 290 citations
- FastTalker: A neural text-to-speech architecture with shallow and group autoregression (2021) — 15 citations
- Expressive TTS Training With Frame and Style Reconstruction Loss (2020) — 85 citations
- Graphspeech: Syntax-Aware Graph Attention Network for Neural Speech Synthesis (2020) — 27 citations
- Modeling Prosodic Phrasing With Multi-Task Learning in Tacotron-Based TTS (2020) — 22 citations
- Teacher-Student Training For Robust Tacotron-Based TTS (2019) — 40 citations
- Adaptive Wavenet Vocoder for Residual Compensation in GAN-Based Voice Conversion (2018) — 38 citations