Ghassen Jerfel

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 109 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 202 citations into it; #25,558 of 35,345 by trust here, #8,893 by citations
• Computer Vision and Pattern Recognition — 78 citations into it
• Representation & Self-Supervised Learning — 51 citations into it
• Probabilistic & Bayesian Methods — 47 citations into it
• Statistics and Probability — 31 citations into it
• Optimization & Training — 30 citations into it
• Model Architectures — 18 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Ghassen Jerfel trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Michael I. Jordan
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ruslan Salakhutdinov
8. Nitish Srivastava
9. J. Urgen Schmidhuber
10. Carl E. Rasmussen

Who trusts Ghassen Jerfel overall

1. Mehrdad Farajtabar
2. Dong Yin
3. Massimo Caccia
4. Oleksiy Ostapenko
5. Mikhail Khodak
6. Jorge Armando Mendez Mendez
7. Pau Rodr'iguez L'opez
8. Seyed Iman Mirzadeh
9. Liyuan Wang
10. Mengye Ren

Papers from Ghassen Jerfel's perspective

Ordered by how strongly the researchers Ghassen Jerfel 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Analyzing the role of model uncertainty for electronic health records (2019) — 123 citations
• Measuring Calibration in Deep Learning (2019) — 692 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors (2020) — 240 citations
• Online gradient-based mixtures for transfer modulation in meta-learning (2018) — 12 citations
• Modulating transfer between tasks in gradient-based meta-learning (2018) — 7 citations
• Reconciling meta-learning and continual learning with online mixtures of tasks (2018) — 113 citations