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Found 5 papers, 3 papers with code
On the Robustness of Randomized Ensembles to Adversarial Perturbations
In this work, we first demystify RECs as we derive fundamental results regarding their theoretical limits, necessary and sufficient conditions for them to be useful, and more.
Adversarial Vulnerability of Randomized Ensembles
In this work we address this question both theoretically and empirically.
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
But these methods are unable to improve throughput (frames-per-second) on real-life hardware while simultaneously preserving robustness to adversarial perturbations.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks
While various complexity reduction techniques, such as lightweight network architecture design and parameter quantization, have been successful in reducing the cost of implementing these networks, these methods have often been considered orthogonal.
