2 articles tagged with #hessian-approximation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce YAQA, a new quantization algorithm that improves model compression by directly optimizing end-to-end error rather than layer-by-layer error. The method achieves 30% error reduction compared to existing approaches like GPTQ and even outperforms quantization-aware training, with theoretical guarantees backing its performance.
Researchers present HF-KCU, a federated learning method that efficiently removes clients' data contributions while maintaining privacy compliance, achieving 47.75x speedup over retraining while preserving model accuracy. The technique uses Krylov subspace approximations and causal weighting to handle data deletion requests in production systems without compromising unaffected participants.
