Maximilian Balandat

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it
• Computer Vision and Pattern Recognition — 56 citations into it
• Optimization & Training — 32 citations into it
• Computational Theory and Mathematics — 30 citations into it
• Management Science and Operations Research — 24 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Neural Architecture Search & AutoML — 8 citations into it

Whom Maximilian Balandat trusts overall

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

1. Peter I. Frazier
2. Donald R. Jones
3. Aaron Klein
4. Diederik P. Kingma
5. Jasper Snoek
6. Geoffrey E. Hinton
7. Joshua D. Knowles
8. Radford M. Neal
9. Andrew Gordon Wilson
10. Frank Hutter

Who trusts Maximilian Balandat overall

1. Min Jin Chong
2. Sam Daulton
3. Colin White
4. D.A. Forsyth
5. Willie Neiswanger

Papers from Maximilian Balandat's perspective

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

Their papers

• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021) — 662 citations
• Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization (2020) — 381 citations
• BoTorch: Programmable Bayesian Optimization in PyTorch (2019) — 102 citations