Ricardo Henao

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 22 papers; trusts 1,186 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 460 citations into it; #6,768 of 35,345 by trust here, #3,757 by citations
• Computer Vision and Pattern Recognition — 325 citations into it; #7,713 of 21,575 by trust here, #3,810 by citations
• Natural Language Processing — 246 citations into it; #2,112 of 5,688 by trust here, #1,583 by citations
• Generative Models — 141 citations into it; #827 of 1,956 by trust here, #418 by citations
• Representation & Self-Supervised Learning — 96 citations into it; #1,638 of 3,145 by trust here, #1,301 by citations
• Model Architectures — 85 citations into it
• Probabilistic & Bayesian Methods — 64 citations into it; #1,389 of 1,525 by trust here, #991 by citations
• Optimization & Training — 48 citations into it

Whom Ricardo Henao trusts overall

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

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

Who trusts Ricardo Henao overall

1. Chunyuan Li
2. Hao Wang
3. Dinghan Shen
4. Zhe Gan
5. Yizhe Zhang
6. Yunchen Pu
7. Fei Ye
8. Lawrence Carin
9. Hao Zhang
10. Dit-Yan Yeung

Papers from Ricardo Henao's perspective

Ordered by how strongly the researchers Ricardo Henao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016) — 851 citations
• Adversarial Feature Matching for Text Generation (2017) — 363 citations
• Learning Generic Sentence Representations Using Convolutional Neural Networks (2016) — 101 citations
• Deep Temporal Sigmoid Belief Networks for Sequence Modeling (2015) — 91 citations
• Adversarial Symmetric Variational Autoencoder (2017) — 81 citations
• Deconvolutional Latent-Variable Model for Text Sequence Matching (2017) — 69 citations
• Deconvolutional Paragraph Representation Learning (2017) — 79 citations
• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (2018) — 44 citations
• Joint Embedding of Words and Labels for Text Classification (2018) — 428 citations
• Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment (2018) — 26 citations
• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018) — 351 citations
• Few-Shot Class-Incremental Learning for Named Entity Recognition (2022) — 27 citations
• Efficient Classification of Very Large Images with Tiny Objects (2021) — 38 citations
• Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification (2021) — 14 citations
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models (2020) — 17 citations
• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018) — 64 citations
• On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018) — 9 citations
• ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (2017) — 227 citations
• Unsupervised Learning of Sentence Representations using Convolutional Neural Networks (2016) — 20 citations
• Learning Deep Sigmoid Belief Networks with Data Augmentation (2015) — 111 citations