Maxim Naumov

Artificial Intelligence · 2007–2021

A researcher in the Science corpus: 11 papers; trusts 70 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #28,413 of 35,345 by trust here, #6,330 by citations
• Computer Vision and Pattern Recognition — 172 citations into it
• Optimization & Training — 55 citations into it
• Model Architectures — 34 citations into it

Whom Maxim Naumov trusts overall

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

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

Who trusts Maxim Naumov overall

1. Dheevatsa Mudigere
2. Bilge Acun
3. Udit Gupta
4. Carole-Jean Wu
5. Chunxing Yin
6. Geet Sethi
7. Michael Lui
8. Zi-Mei Yao
9. Xiaodong Wang
10. Antonio A. Ginart

Papers from Maxim Naumov's perspective

Ordered by how strongly the researchers Maxim Naumov 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems (2020) — 107 citations
• AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks (2017) — 156 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale (2021) — 14 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
• Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems (2019) — 114 citations
• The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (2019) — 330 citations
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations
• Atomistic Simulation of Realistically Sized Nanodevices Using NEMO 3-D—Part I: Models and Benchmarks (2007) — 219 citations