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Found 7 papers, 2 papers with code
Large Language Models Synergize with Automated Machine Learning
In addition, autoML significantly improves the performance of the generated ML programs.
Exploring the Improvement of Evolutionary Computation via Large Language Models
Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains.
Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation
Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions.
Multi-role Consensus through LLMs Discussions for Vulnerability Detection
Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance.
Value Iteration Networks with Gated Summarization Module
We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: (1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations, and (2) introducing a Gated Summarization module to summarize the iterative process.
OACAL: Finding Module-consistent Specifications to Secure Systems from Weakened User Obligations
To improve the security with the awareness of unexpected user behaviors, a system can be redesigned to a more robust one by changing the order of actions in its specification.
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.
