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Found 12 papers, 4 papers with code
DUPRE: Data Utility Prediction for Efficient Data Valuation
Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models.
Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities
Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks.
Global Challenge for Safe and Secure LLMs Track 1
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities.
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies.
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
SparGE measures similarity by jointly sparse coding and graph embedding.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method.
Utilizing Temporal Information for Taxonomy Construction
Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms.
