Sebastian Pokutta
Artificial Intelligence · 2016–2025
A researcher in the Science corpus: 16 papers; trusts 299 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 179 citations into it; #11,403 of 35,345 by trust here, #17,776 by citations
- Optimization & Training — 157 citations into it; #1,045 of 2,601 by trust here, #1,419 by citations
- Computational Mechanics — 79 citations into it; #3,509 of 10,001 by trust here, #4,039 by citations
- Computational Theory and Mathematics — 68 citations into it; #4,744 of 12,182 by trust here, #8,294 by citations
- Numerical Analysis — 48 citations into it; #1,629 of 3,586 by trust here, #1,372 by citations
- Computer Vision and Pattern Recognition — 39 citations into it
- Management Science and Operations Research — 30 citations into it; #7,525 of 11,946 by trust here, #6,563 by citations
- Atomic and Molecular Physics, and Optics — 26 citations into it
Whom Sebastian Pokutta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Martin Jaggi
- Yurii E. Nesterov
- Simon Lacoste-Julien
- Dan Garber
- Elad Hazan
- Boris Polyak
- Alex Krizhevsky
- Dimitri P. Bertsekas
- Geoffrey E. Hinton
- Robert Tibshirani
Who trusts Sebastian Pokutta overall
- Thomas Kerdreux
- Cyrille W. Combettes
- Alejandro Carderera
- Hassan Mortagy
- Alexandre d'Aspremont
- Emilio Carrizosa
- S. Ilker Birbil
- Lewis Liu
- Francesco Rinaldi
- Swati Gupta
Papers from Sebastian Pokutta's perspective
Ordered by how strongly the researchers Sebastian Pokutta 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Reinforcement Learning under Model Mismatch (2017) — 94 citations
- Lazifying Conditional Gradient Algorithms (2016) — 59 citations
- IPBoost - Non-Convex Boosting via Integer Programming (2020) — 6 citations
- Complexity of Linear Minimization and Projection on Some Sets (2021) — 58 citations
- S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment (2025)
- The Frank-Wolfe Algorithm: A Short Introduction (2023) — 48 citations
- Challenges and opportunities in quantum optimization (2023) — 341 citations
- Linear Bandits on Uniformly Convex Sets (2021) — 10 citations
- Local and Global Uniform Convexity Conditions (2021) — 16 citations
- Projection-Free Optimization on Uniformly Convex Sets (2020) — 48 citations
- Projection-Free Adaptive Gradients for Large-Scale Optimization (2020) — 13 citations
- Boosting Frank-Wolfe by Chasing Gradients (2020) — 34 citations
- Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization (2020) — 8 citations
- Second-order Conditional Gradients (2020) — 4 citations
- Blended Conditonal Gradients (2019)
- Blended Conditional Gradients: the unconditioning of conditional gradients (2018) — 31 citations