Justin Patriquin

Artificial Intelligence · 2018

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

Subfields

Whom Justin Patriquin trusts

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

  1. Payman Mohassel (in Artificial Intelligence)

Papers from Justin Patriquin's perspective

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

  1. SecureML: A System for Scalable Privacy-Preserving Machine Learning
  2. ABY3: A Mixed Protocol Framework for Machine Learning
  3. Multiparty Computation from Somewhat Homomorphic Encryption
  4. Gazelle: A Low Latency Framework for Secure Neural Network Inference
  5. TensorFlow: A system for large-scale machine learning
  6. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
  7. Extending Oblivious Transfers Efficiently
  8. The Knowledge Complexity of Interactive Proof Systems
  9. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
  10. DeepSecure: Scalable Provably-Secure Deep Learning

Their papers