Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.
Trace Norm Regularised Deep Multi-Task Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Joint Detection of Malicious Domains and Infected Clients
Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.