Erik Nijkamp

Artificial Intelligence · 2011–2023

A researcher in the Science corpus: 17 papers; trusts 1,009 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 360 citations into it; #4,580 of 35,345 by trust here, #4,682 by citations
• Computer Vision and Pattern Recognition — 308 citations into it; #3,504 of 21,575 by trust here, #3,692 by citations
• Generative Models — 239 citations into it; #830 of 1,956 by trust here, #1,021 by citations
• Probabilistic & Bayesian Methods — 152 citations into it; #404 of 1,525 by trust here, #451 by citations
• Natural Language Processing — 95 citations into it
• Optimization & Training — 82 citations into it; #1,097 of 2,601 by trust here, #988 by citations
• Model Architectures — 56 citations into it
• Molecular Biology — 56 citations into it

Whom Erik Nijkamp trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Song-Chun Zhu
5. Yann LeCun
6. Alex Krizhevsky
7. Radford M. Neal
8. Max Welling
9. Kaiming He
10. Ilya Sutskever

Who trusts Erik Nijkamp overall

1. Zhisheng Xiao
2. Yang Song
3. Will Grathwohl
4. Yilun Du
5. Ruiqi Gao
6. Mitch Hill
7. Song-Chun Zhu
8. Sam Bond-Taylor
9. Tian Han
10. Kuan-Chieh Jackson Wang

Papers from Erik Nijkamp's perspective

Ordered by how strongly the researchers Erik Nijkamp 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model (2019) — 236 citations
• On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models (2019) — 185 citations
• Flow Contrastive Estimation of Energy-Based Models (2019) — 129 citations
• Large-scale matrix factorization with distributed stochastic gradient descent (2011) — 704 citations
• Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model (2018) — 74 citations
• Learning Latent Space Energy-Based Prior Model (2020) — 146 citations
• Joint Training of Variational Auto-Encoder and Latent Energy-Based Model (2020) — 63 citations
• ProGen2: Exploring the Boundaries of Protein Language Models (2022) — 555 citations
• CodeGen2: Lessons for Training LLMs on Programming and Natural Languages (2023) — 243 citations
• CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis (2022) — 1,548 citations
• A Conversational Paradigm for Program Synthesis (2022) — 194 citations
• Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC (2020) — 16 citations
• Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020) — 90 citations
• Deep Learning With TensorFlow: A Review (2019) — 461 citations
• A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks (2019) — 16 citations
• On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model (2019) — 29 citations
• Building a Telescope to Look Into High-Dimensional Image Spaces (2018) — 10 citations