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Found 9 papers, 1 papers with code
Comparative Graph-based Summarization of Scientific Papers Guided by Comparative Citations
And then, we propose the comparative graph-based summarization (CGSUM) method to create comparative summaries using citations as guidance.
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning
Reinforcement learning (RL) methods typically learn new tasks from scratch, often disregarding prior knowledge that could accelerate the learning process.
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models
With the growing popularity of LLMs among the general public users, privacy-preserving and adversarial robustness have become two pressing demands for LLM-based services, which have largely been pursued separately but rarely jointly.
Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense
Large Language Models (LLMs) have showcased remarkable capabilities across various domains.
SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
Equipped with a dual embedding learning module and a structure perception matching module, SEGMN achieves structure enhancement in both embedding learning and cross-graph matching.
LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks
To enhance the validity of LLM-generated plans, visual demonstrations and online videos have been widely employed to guide the planning process.
Comparative Study on Semi-supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System
This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems.
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
We propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetrical behaviors and language instructions.
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network
To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection.
