John Palowitch

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Graph Learning — 23 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Molecular Biology — 5 citations into it

Whom John Palowitch trusts overall

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

1. Mark Newman
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Jure Leskovec
5. Diederik P. Kingma
6. Weihua Hu
7. Rex Ying
8. Michael U Gutmann
9. Alex Krizhevsky
10. Peter J. Bickel

Papers from John Palowitch's perspective

Ordered by how strongly the researchers John Palowitch 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Dropout: a simple way to prevent neural networks from overfitting
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Graph Clustering with Graph Neural Networks (2020) — 410 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations