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Found 7 papers, 5 papers with code
DroidCall: A Dataset for LLM-powered Android Intent Invocation
Given a task instruction in natural language, small language models such as Qwen2. 5-3B and Gemma2-2B fine-tuned with DroidCall can approach or even surpass the capabilities of GPT-4o for accurate Android intent invocation.
LlamaTouch: A Faithful and Scalable Testbed for Mobile UI Task Automation
LlamaTouch comprises three key techniques: (1) On-device task execution that enables mobile agents to interact with realistic mobile environments for task execution.
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment.
Mobile Foundation Model as Firmware
Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.
Boosting the Discriminant Power of Naive Bayes
In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes.
A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes
Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data.
A Semi-Supervised Adaptive Discriminative Discretization Method Improving Discrimination Power of Regularized Naive Bayes
Data discretization is important in naive Bayes.
