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Found 6 papers, 2 papers with code
On Redundancy and Diversity in Cell-based Neural Architecture Search
Searching for the architecture cells is a dominant paradigm in NAS.
Approximate Neural Architecture Search via Operation Distribution Learning
The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections.
NAS evaluation is frustratingly hard
As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others.
Encrypted accelerated least squares regression
Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods.
Encrypted statistical machine learning: new privacy preserving methods
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data.
A review of homomorphic encryption and software tools for encrypted statistical machine learning
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt.
