Minje Kim

Signal Processing · 2014–2022

A researcher in the Science corpus: 7 papers; trusts 98 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 77 citations into it; #507 of 7,572 by trust here, #709 by citations
• Speech & Audio — 52 citations into it
• Artificial Intelligence — 50 citations into it; #4,307 of 35,345 by trust here, #7,172 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #14,668 of 21,575 by trust here, #13,815 by citations
• Model Architectures — 16 citations into it
• Computational Mechanics — 13 citations into it
• Optimization & Training — 11 citations into it
• Generative Models — 10 citations into it

Whom Minje Kim trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Diederik P. Kingma
8. Ronald J. Williams
9. Kaiming He
10. Nitish Srivastava

Who trusts Minje Kim overall

1. Jungwook Choi
2. Ruohan Gao
3. Michael Pfeiffer
4. Mohammad Rastegari
5. Wei-Yao Tang
6. Kai Zhen
7. Xiao-Lei Zhang
8. Tejalal Choudhary
9. Zhongchao Qian
10. Amartya Sanyal

Papers from Minje Kim's perspective

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

Their papers

• Bitwise Neural Networks (2016) — 226 citations
• Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation (2015) — 467 citations
• Deep learning for monaural speech separation (2014) — 458 citations
• Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding (2022) — 9 citations
• Harp-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding (2021) — 19 citations
• Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding (2020) — 28 citations
• Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding (2019) — 43 citations