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Found 6 papers, 2 papers with code
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization
Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e. g., each subgraph can only contain at most one complex operator.
Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations
Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware.
Automating Botnet Detection with Graph Neural Networks
Botnets are now a major source for many network attacks, such as DDoS attacks and spam.
An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning
ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise.
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
