Kshitiz Malik

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it
• Optimization & Training — 45 citations into it
• Federated & Distributed Learning — 43 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Electrical and Electronic Engineering — 9 citations into it

Whom Kshitiz Malik trusts overall

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

1. Alex Krizhevsky
2. H. Brendan McMahan
3. Geoffrey E. Hinton
4. Sebastian U. Stich
5. Kaiming He
6. Yoshua Bengio
7. Sepp Hochreiter
8. Yann LeCun
9. Diederik P. Kingma
10. Hao Yu

Papers from Kshitiz Malik's perspective

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

Their papers

• Federated User Representation Learning (2019) — 73 citations
• Papaya: Practical, Private, and Scalable Federated Learning (2021) — 172 citations
• Federated Learning with Buffered Asynchronous Aggregation (2021) — 469 citations