Danielle C. Maddix

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 5 papers; trusts 46 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #17,773 of 35,345 by trust here, #16,166 by citations
• Signal Processing — 62 citations into it; #3,616 of 7,572 by trust here, #2,400 by citations
• Time-Series & Forecasting — 57 citations into it; #378 of 381 by trust here, #199 by citations
• Management Science and Operations Research — 51 citations into it; #11,635 of 11,946 by trust here, #4,746 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #15,157 of 21,575 by trust here, #18,782 by citations
• Probabilistic & Bayesian Methods — 40 citations into it
• Model Architectures — 33 citations into it
• Generative Models — 23 citations into it

Whom Danielle C. Maddix trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Danilo Jimenez Rezende
4. Matthias W. Seeger
5. Robert Fildes
6. Rahul G. Krishnan
7. Yoshua Bengio
8. Rob J Hyndman
9. Yann LeCun
10. Max Welling

Who trusts Danielle C. Maddix overall

1. Haixu Wu
2. Yong Liu
3. Bernie Wang
4. Yitian Chen
5. SHIYANG LI
6. Jianmin Wang
7. Bryan Lim
8. Mingsheng Long
9. Jiehui Xu
10. Yanfei Kang

Papers from Danielle C. Maddix's perspective

Ordered by how strongly the researchers Danielle C. Maddix 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. 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. Learning Multiple Layers of Features from Tiny Images
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

• Deep Factors with Gaussian Processes for Forecasting (2018) — 47 citations
• GluonTS: Probabilistic Time Series Models in Python (2019) — 131 citations
• Deep Factors for Forecasting (2019) — 205 citations
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — 391 citations
• Chronos: Learning the Language of Time Series (2024) — 878 citations