Sebastian Goldt

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 43 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it
• Scaling & Learning Theory — 56 citations into it
• Optimization & Training — 38 citations into it
• Statistics and Probability — 30 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Statistical and Nonlinear Physics — 29 citations into it
• Statistical Learning Theory — 27 citations into it
• Model Architectures — 17 citations into it

Whom Sebastian Goldt trusts overall

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

1. Yann LeCun
2. Lénaïc Chizat
3. Geoffrey E. Hinton
4. Song Mei
5. Yoshua Bengio
6. Andrew M. Saxe
7. Yuanzhi Li
8. Michael Biehl
9. Alex Krizhevsky
10. Francis R. Bach

Who trusts Sebastian Goldt overall

1. Mehdi Abbana Bennani
2. Stéphane d'Ascoli
3. Masashi Sugiyama
4. Levent Sagun
5. Marylou Gabrié
6. J. Paccolat

Papers from Sebastian Goldt's perspective

Ordered by how strongly the researchers Sebastian Goldt 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup (2019) — 186 citations
• Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (2021) — 103 citations
• Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model (2021) — 35 citations
• Modelling the influence of data structure on learning in neural networks (2019) — 48 citations