Milan Vojnovic

Optimization & Training · 2005–2017

A researcher in the Science corpus: 8 papers; trusts 75 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 55 citations into it; #665 of 2,601 by trust here, #458 by citations
• Artificial Intelligence — 49 citations into it; #17,413 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #12,602 of 21,575 by trust here, #8,122 by citations
• Computer Networks and Communications — 24 citations into it; #3,026 of 9,886 by trust here, #3,318 by citations
• Computational Mechanics — 17 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Model Architectures — 12 citations into it

Whom Milan Vojnovic trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Mark Newman
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Sébastien Bubeck
7. Sergey Ioffe
8. Sepp Hochreiter
9. Nikko Ström
10. Ilya Sutskever

Who trusts Milan Vojnovic overall

1. Jean-Pierre Hubaux
2. Sebastian U. Stich
3. Ahmed Khaled
4. Peter Richtárik
5. Esa Hyytiä
6. Stuart H. Kurkowski
7. Antonio Polino
8. Konstantin Mishchenko
9. Mirco Musolesi
10. Amirhossein Reisizadeh

Papers from Milan Vojnovic's perspective

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

Their papers

• QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks (2016) — 391 citations
• Perfect simulation and stationarity of a class of mobility models (2005) — 520 citations
• Power Law and Exponential Decay of Intercontact Times between Mobile Devices (2007) — 762 citations
• Crowdsourcing and all-pay auctions (2009) — 322 citations
• FENNEL: streaming graph partitioning for massive scale graphs (2014) — 372 citations
• Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks (2017) — 7 citations
• QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent (2016) — 125 citations
• QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding (2016) — 1,728 citations