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Found 4 papers, 1 papers with code
Improving Continuous Normalizing Flows using a Multi-Resolution Framework
Recent work has shown that Continuous Normalizing Flows (CNFs) can serve as generative models of images with exact likelihood calculation and invertible generation/density estimation.
EuclidNets: Combining hardware and architecture design for Efficient Inference and Training
In order to deploy deep neural networks on edge devices, compressed (resource efficient) networks need to be developed.
Uncertainty for deep image classifiers on out of distribution data.
In addition to achieving high accuracy, in many applications, it is important to estimate the probability that a model prediction is correct.
Adversarial Boot Camp: label free certified robustness in one epoch
Machine learning models are vulnerable to adversarial attacks.
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