Martin Renqiang Min

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 13 papers; trusts 919 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #21,913 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 161 citations into it
• Natural Language Processing — 142 citations into it; #4,539 of 5,688 by trust here, #3,754 by citations
• Representation & Self-Supervised Learning — 63 citations into it
• Molecular Biology — 60 citations into it; #833 of 17,773 by trust here, #850 by citations
• Model Architectures — 40 citations into it
• Multimodal Learning — 27 citations into it
• Generative Models — 23 citations into it

Whom Martin Renqiang Min trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Ruslan Salakhutdinov
6. Yann LeCun
7. Alex Krizhevsky
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Martin Renqiang Min overall

1. Dinghan Shen
2. Wei Liu
3. Zheda Mai
4. Dongsub Shim
5. Jake Snell
6. Manu N. Setty
7. Lucas D. Ward
8. Raymond G. Cavalcante
9. Yifeng Li
10. David R. Kelley

Papers from Martin Renqiang Min's perspective

Ordered by how strongly the researchers Martin Renqiang Min 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Video Generation From Text (2017) — 317 citations
• A Deep Non-linear Feature Mapping for Large-Margin kNN Classification (2009) — 97 citations
• Adaptive Feature Abstraction for Translating Video to Language (2016) — 14 citations
• Learning Context-Sensitive Convolutional Filters for Text Processing (2017) — 23 citations
• Architecture of the human regulatory network derived from ENCODE data (2012) — 1,495 citations
• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018) — 351 citations
• An Integrated Encyclopedia of DNA Elements in the Human Genome (2012) — 16,840 citations
• High-order neural networks and kernel methods for peptide-MHC binding prediction (2015) — 35 citations
• On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018) — 9 citations
• Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection (2018) — 2,127 citations
• Adaptive Convolutional Filter Generation for Natural Language Understanding (2017) — 11 citations
• Adaptive Feature Abstraction for Translating Video to Text (2016) — 29 citations
• Deep Supervised t-Distributed Embedding (2010) — 59 citations