Saurav Prakash

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 35 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #34,275 of 35,345 by trust here, #21,316 by citations
• Computer Networks and Communications — 46 citations into it; #9,540 of 9,886 by trust here, #5,518 by citations
• Optimization & Training — 24 citations into it
• ML Systems & Hardware — 23 citations into it
• Information Systems — 16 citations into it
• Computational Mechanics — 15 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Hardware and Architecture — 9 citations into it

Whom Saurav Prakash trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Andreas Holzinger
4. Ali Rahimi
5. David John Cameron MacKay
6. Xavier Glorot
7. Yoshua Bengio
8. Kaiming He
9. H. Brendan McMahan
10. Geoffrey E. Hinton

Who trusts Saurav Prakash overall

1. Amirhossein Reisizadeh
2. Songze Li
3. Chaoyang He
4. Yuanming Shi
5. Tian Li
6. Can Bülent Karakus

Papers from Saurav Prakash's perspective

Ordered by how strongly the researchers Saurav Prakash 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Coded Computing for Federated Learning at the Edge (2020) — 11 citations
• Mitigating Byzantine Attacks in Federated Learning (2020) — 44 citations
• CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning (2019) — 30 citations
• Coded Computation Over Heterogeneous Clusters (2019) — 21 citations
• Coded computation over heterogeneous clusters (2017) — 246 citations