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Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
In this work, we develop an adaptive compression-aware split learning method ('deprune') to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources.
Leveraging Machine Learning for Multichain DeFi Fraud Detection
Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries.
Monetary Policy, Digital Assets, and DeFi Activity
We also show that some borrowing interest rates in the Ethereum DeFi ecosystem are affected positively by unexpected changes in monetary policy.
An Overview of the Data-Loader Landscape: Comparative Performance Analysis
Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs.
