Isaac Elias

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #13,079 of 35,345 by trust here, #12,387 by citations
• Signal Processing — 47 citations into it; #4,164 of 7,572 by trust here, #2,589 by citations
• Speech & Audio — 43 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 24 citations into it
• Generative Models — 22 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it

Whom Isaac Elias trusts overall

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

1. Alex Graves
2. Ronald J. Williams
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Dzmitry Bahdanau
6. Diederik P. Kingma
7. Jimmy Ba
8. Kyunghyun Cho
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Isaac Elias overall

1. Ye Jia
2. Xu Tan
3. Michelle Tadmor Ramanovich
4. Tal Remez
5. Chenpeng Du
6. Zhaoxi Mu
7. Cong Han
8. Tao Qin
9. Roi Pomerantz
10. Sei Ueno

Papers from Isaac Elias's perspective

Ordered by how strongly the researchers Isaac Elias 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling (2020) — 116 citations
• Parallel Tacotron: Non-Autoregressive and Controllable TTS (2020) — 110 citations
• Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling (2021) — 85 citations