Brando Miranda

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #25,151 of 35,345 by trust here, #9,369 by citations
• Scaling & Learning Theory — 26 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Brando Miranda trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Ruslan Salakhutdinov
7. Brenden M. Lake
8. Tomaso A. Poggio
9. Francis R. Bach
10. Ilya Sutskever

Who trusts Brando Miranda overall

1. Leonard Bereska

Papers from Brando Miranda's perspective

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

Their papers

• Theory of Deep Learning III: explaining the non-overfitting puzzle (2017) — 138 citations
• Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review (2016) — 657 citations
• Are Emergent Abilities of Large Language Models a Mirage? (2023) — 707 citations