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Found 4 papers, 4 papers with code
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques
GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase?
DASS: Differentiable Architecture Search for Sparse neural networks
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices
This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design.
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
Here, we consider LVMs that are defined for a range of different distributions, i. e., observables can follow any (regular) distribution of the exponential family.
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