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Found 4 papers, 2 papers with code
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
Isolation mechanisms for high-speed packet-processing pipelines
We have open sourced the code for Menshen's hardware and software at https://isolation. quest/.
Networking and Internet Architecture Hardware Architecture
How to Train your DNN: The Network Operator Edition
Deep Neural Nets have hit quite a crest, But physical networks are where they must rest, And here we put them all to the test, To see which network optimization is best.
