D. Narayanan
Artificial Intelligence · with Zhang · 2016–2023
A researcher in the Science corpus: 16 papers; trusts 313 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 405 citations into it; #16,372 of 35,345 by trust here, #1,267 by citations
- Computer Vision and Pattern Recognition — 214 citations into it; #14,784 of 21,575 by trust here, #3,489 by citations
- Natural Language Processing — 144 citations into it
- Optimization & Training — 110 citations into it; #2,166 of 2,601 by trust here, #759 by citations
- Evaluation & Benchmarks — 88 citations into it
- Large Language Models — 83 citations into it
- Efficiency & Compression — 76 citations into it; #1,157 of 1,264 by trust here, #325 by citations
- Computer Networks and Communications — 70 citations into it
Whom D. Narayanan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Karen Simonyan
- Ilya Sutskever
- Yoshua Bengio
- Yann LeCun
- Ross B. Girshick
- Sergey Ioffe
Who trusts D. Narayanan overall
- Samyam Rajbhandari
- Vijay Anand Korthikanti
- Keshav Santhanam
- Trevor Gale
- Jared Casper
- Can Bülent Karakus
- Zhen Zhang
- Shaden Smith
- Mohammad Shoeybi
- Matei A. Zaharia
Papers from D. Narayanan's perspective
Ordered by how strongly the researchers D. Narayanan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM (2021) — 1,287 citations
- PipeDream: generalized pipeline parallelism for DNN training (2019) — 1,228 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- Memory-Efficient Pipeline-Parallel DNN Training (2020) — 299 citations
- Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark (2018) — 126 citations
- PipeDream: Fast and Efficient Pipeline Parallel DNN Training (2018) — 317 citations
- MLPerf Training Benchmark (2019) — 388 citations
- Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads (2020) — 300 citations
- MegaBlocks: Efficient Sparse Training with Mixture-of-Experts (2022) — 234 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Efficient Large-Scale Language Model Training on GPU Clusters (2021) — 108 citations
- PipeDream: Pipeline Parallelism for DNN Training (2018)
- DAWNBench : An End-to-End Deep Learning Benchmark and Competition (2017) — 353 citations
- MacroBase: Analytic Monitoring for the Internet of Things (2016) — 9 citations
- MacroBase: Prioritizing Attention in Fast Data (2016) — 140 citations