5 articles tagged with #tensor-decomposition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed TT-SEAL, a selective encryption framework for compressed AI models using Tensor-Train Decomposition that maintains security while encrypting only 4.89-15.92% of parameters. The system achieves the same robustness as full encryption while reducing AES decryption overhead in end-to-end latency from 58% to as low as 2.76%.
Researchers present DelayMix, an online machine learning framework that models streaming time series as dynamic mixtures of time-delay systems, enabling rapid adaptation to regime shifts while maintaining memory efficiency. The method uses tensor decomposition to capture system dynamics and input delays, demonstrating superior forecasting accuracy on non-stationary data compared to existing approaches.
Researchers introduce MATU, a novel uncertainty quantification framework using tensor decomposition to address reliability challenges in Large Language Model-based Multi-Agent Systems. The method analyzes entire reasoning trajectories rather than single outputs, effectively measuring uncertainty across different agent structures and communication topologies.
Researchers developed a hybrid AI approach combining tensor decomposition with neural networks to improve MIMO channel estimation for 6G wireless systems under pilot signal limitations. The method achieves significant performance improvements over traditional approaches, with up to 13.11 dB better accuracy in specific scenarios.
Researchers propose a reparameterized Tensor Ring functional decomposition method that uses Implicit Neural Representations to improve multi-dimensional data recovery tasks. The approach addresses limitations in high-frequency modeling through structured reparameterization and demonstrates superior performance in image processing and point cloud recovery applications.
