Enea Ceolini
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 193 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it; #5,055 of 35,345 by trust here, #7,062 by citations
- Safety, Robustness & Interpretability — 49 citations into it; #341 of 2,251 by trust here, #593 by citations
- Computer Vision and Pattern Recognition — 45 citations into it
- Biophysics — 16 citations into it
- Signal Processing — 16 citations into it
- Natural Language Processing — 13 citations into it
- Model Architectures — 8 citations into it
- Evaluation & Benchmarks — 7 citations into it
Whom Enea Ceolini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matthew D. Zeiler
- Alex Krizhevsky
- Yann LeCun
- Geoffrey E. Hinton
- Christian Szegedy
- Grgoire Montavon
- Avanti Shrikumar
- Karen Simonyan
- Yoshua Bengio
- Ilya Sutskever
Who trusts Enea Ceolini overall
- Reza Shokri
- Wojciech Samek
- Laura Rieger
- Arun Das
- Martin Strobel
- Gabrielle Ras
- Patrick Hall
- Julius Adebayo
- Boris Sharchilev
- Grgoire Montavon
Papers from Enea Ceolini's perspective
Ordered by how strongly the researchers Enea Ceolini trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A unified view of gradient-based attribution methods for Deep Neural Networks (2017) — 149 citations
- Towards better understanding of gradient-based attribution methods for Deep Neural Networks (2017) — 1,034 citations
- Gradient-Based Attribution Methods (2019) — 191 citations
- FaSNet: Low-Latency Adaptive Beamforming for Multi-Microphone Audio Processing (2019) — 186 citations