D. Lim

Artificial Intelligence · with Kim · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #15,804 of 35,345 by trust here, #19,873 by citations
• Signal Processing — 50 citations into it; #3,591 of 7,572 by trust here, #3,913 by citations
• Speech & Audio — 40 citations into it; #983 of 1,076 by trust here, #900 by citations
• Generative Models — 28 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Model Architectures — 12 citations into it

Whom D. Lim trusts overall

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

1. Diederik P. Kingma
2. Adrian La'ncucki
3. Jaehyeon Kim
4. Ryuichi Yamamoto
5. Aäron van den Oord
6. Geoffrey E. Hinton
7. Jungil Kong
8. Yoshua Bengio
9. Ashish Vaswani
10. Alex Graves

Who trusts D. Lim overall

1. Xu Tan
2. Cong Han
3. Yi Ren
4. Zhaoxi Mu
5. Hubert Siuzdak
6. Jonathan Shen
7. Sang-gil Lee
8. Isaac Elias
9. Tao Qin
10. Won Jang

Papers from D. Lim's perspective

Ordered by how strongly the researchers D. Lim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech (2022) — 75 citations
• UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation (2021) — 200 citations
• Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains (2020) — 42 citations
• JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment (2020) — 40 citations