Gabriele Abbati

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #26,652 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #16,333 of 21,575 by trust here, #13,815 by citations
• Representation & Self-Supervised Learning — 45 citations into it; #2,496 of 3,145 by trust here, #2,355 by citations
• Generative Models — 25 citations into it
• Signal Processing — 18 citations into it
• Statistics and Probability — 16 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Gabriele Abbati trusts overall

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

1. Geoffrey E. Hinton
2. Zoubin Ghahramani
3. Yoshua Bengio
4. Radford M. Neal
5. Diederik P. Kingma
6. Yann LeCun
7. Alex Graves
8. Alex Krizhevsky
9. J. Urgen Schmidhuber
10. Ilya Sutskever

Who trusts Gabriele Abbati overall

1. Francesco Locatello
2. Enyan Dai
3. Bernhard Schölkopf
4. Frederik Trauble
5. Stefan Bauer

Papers from Gabriele Abbati's perspective

Ordered by how strongly the researchers Gabriele Abbati trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On the Fairness of Disentangled Representations (2019) — 250 citations
• AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs (2019) — 15 citations
• MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting (2018) — 15 citations