Blake A. Hechtman

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 9 papers; trusts 214 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #25,588 of 35,345 by trust here, #2,492 by citations
• Computer Vision and Pattern Recognition — 115 citations into it; #11,476 of 21,575 by trust here, #3,124 by citations
• Optimization & Training — 58 citations into it
• Model Architectures — 57 citations into it; #1,666 of 2,278 by trust here, #1,131 by citations
• Efficiency & Compression — 39 citations into it; #793 of 1,264 by trust here, #202 by citations
• Hardware and Architecture — 31 citations into it; #1,214 of 1,551 by trust here, #97 by citations
• Natural Language Processing — 22 citations into it
• ML Systems & Hardware — 19 citations into it

Whom Blake A. Hechtman trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yoshua Bengio
7. Ross B. Girshick
8. Noam Shazeer
9. Alex Graves
10. Yann LeCun

Who trusts Blake A. Hechtman overall

1. Yi Tay
2. Lu Yuan
3. Donald Metzler
4. Jianfeng Gao
5. Jianwei Yang
6. Bin Xiao
7. Dara Bahri
8. Ting-Li Chen

Papers from Blake A. Hechtman's perspective

Ordered by how strongly the researchers Blake A. Hechtman 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Scaling Local Self-Attention for Parameter Efficient Visual Backbones (2021) — 476 citations
• GSPMD: General and Scalable Parallelization for ML Computation Graphs (2021) — 188 citations
• Mesh-TensorFlow: Deep Learning for Supercomputers (2018) — 439 citations
• Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models (2022) — 117 citations
• Unified Scaling Laws for Routed Language Models (2022) — 285 citations
• Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training (2020) — 52 citations
• Scale MLPerf-0.6 models on Google TPU-v3 Pods (2019) — 43 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations