Young-Sik Kim

Artificial Intelligence · with Lee · 2021

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Young-Sik Kim trusts

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

  1. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  2. Craig Gentry (in Artificial Intelligence)

Papers from Young-Sik Kim's perspective

Ordered by how strongly the researchers Young-Sik Kim trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Fully homomorphic encryption using ideal lattices
  2. Learning Multiple Layers of Features from Tiny Images
  3. Somewhat Practical Fully Homomorphic Encryption
  4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
  5. Bootstrapping for Approximate Homomorphic Encryption (IACR Cryptology ePrint Archive)
  6. CryptoDL: Deep Neural Networks over Encrypted Data
  7. Gazelle: A Low Latency Framework for Secure Neural Network Inference
  8. A Full RNS Variant of Approximate Homomorphic Encryption
  9. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Puerto Rico June pages Ego Motion Estimation Using Optical Flow Fields Observed from Multiple Cameras
  10. Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

Their papers