Ronny Luss
Artificial Intelligence · 2007–2019
A researcher in the Science corpus: 8 papers; trusts 165 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 103 citations into it; #21,179 of 35,345 by trust here, #8,283 by citations
- Safety, Robustness & Interpretability — 49 citations into it
- Computer Vision and Pattern Recognition — 42 citations into it; #15,330 of 21,575 by trust here, #16,787 by citations
- Natural Language Processing — 17 citations into it
- Optimization & Training — 15 citations into it
- Computational Mechanics — 12 citations into it; #6,965 of 10,001 by trust here, #7,501 by citations
- Signal Processing — 12 citations into it
- Biophysics — 11 citations into it
Whom Ronny Luss trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cheng Soon Ong
- Geoffrey E. Hinton
- Thorsten Joachims
- Michael I. Jordan
- Alex Smola
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Alex Krizhevsky
- Arthur U. Asuncion
Who trusts Ronny Luss overall
- Xiao Ding
- Leonardo dos Santos Pinheiro
- Ting-Wei Liu
- Takashi Matsubara
- Giannis Nikolentzos
- Titouan Vayer
- Fariza Hanum Binti Md Nasaruddin
- Junwen Duan
- Mark Dras
- Amit Dhurandhar
Papers from Ronny Luss's perspective
Ordered by how strongly the researchers Ronny Luss 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (2018) — 687 citations
- Predicting abnormal returns from news using text classification (2008) — 157 citations
- One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques (2019) — 478 citations
- Improving Simple Models with Confidence Profiles (2018) — 49 citations
- TIP: Typifying the Interpretability of Procedures (2017) — 35 citations
- Interpretable Policies for Dynamic Product Recommendations (2016) — 20 citations
- Sparse Quantile Huber Regression for Efficient and Robust Estimation (2014) — 19 citations
- Support vector machine classification with indefinite kernels (2007) — 141 citations