Jie Jessie Ren
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 7 papers; trusts 297 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 145 citations into it; #21,100 of 35,345 by trust here, #7,112 by citations
- Computer Vision and Pattern Recognition — 70 citations into it; #16,857 of 21,575 by trust here, #8,831 by citations
- Molecular Biology — 67 citations into it; #17,508 of 17,773 by trust here, #7,298 by citations
- Safety, Robustness & Interpretability — 45 citations into it; #1,337 of 2,251 by trust here, #505 by citations
- Natural Language Processing — 41 citations into it
- Ecology — 30 citations into it
- Evaluation & Benchmarks — 27 citations into it
- Probabilistic & Bayesian Methods — 22 citations into it
Whom Jie Jessie Ren trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Dan Hendrycks
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Ilya Sutskever
- Jimmy Ba
- Alex Graves
Who trusts Jie Jessie Ren overall
- Stanislav Fort
- Jingkang Yang
- Joost R. van Amersfoort
- Erik A. Daxberger
- Yixuan Li
- Alexander Meinke
- Kristopher Kieft
- Saikiran Bulusu
- Zhen Fang
- Balaji Lakshminarayanan
Papers from Jie Jessie Ren's perspective
Ordered by how strongly the researchers Jie Jessie Ren trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019) — 2,269 citations
- Likelihood Ratios for Out-of-Distribution Detection (2019) — 840 citations
- Exploring the Limits of Out-of-Distribution Detection (2021) — 428 citations
- A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection (2021) — 310 citations
- VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data (2017) — 580 citations
- Identifying viruses from metagenomic data using deep learning (2020) — 553 citations
- SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders (2019) — 20 citations