Vladimir Braverman

Artificial Intelligence · with Arora · 2020–2024

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Optimization & Training — 20 citations into it
• Federated & Distributed Learning — 19 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Efficiency & Compression — 14 citations into it
• Electrical and Electronic Engineering — 8 citations into it

Whom Vladimir Braverman trusts overall

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

1. Alex Krizhevsky

Papers from Vladimir Braverman's perspective

Ordered by how strongly the researchers Vladimir Braverman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning Multiple Layers of Features from Tiny Images
2. Large Scale Distributed Deep Networks
3. Language Models are Few-Shot Learners (Neural Information Processing Systems)
4. On the importance of initialization and momentum in deep learning
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet classification with deep convolutional neural networks
7. PyTorch: An Imperative Style, High-Performance Deep Learning Library
8. Gradient-based learning applied to document recognition
9. LLaMA: Open and Efficient Foundation Language Models (arXiv.org)
10. Long Short-Term Memory

Their papers

• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache (2024) — 545 citations
• FetchSGD: Communication-Efficient Federated Learning with Sketching (2020) — 432 citations