DP-SelFT improves the privacy-utility trade-off for LLM fine-tuning by selecting robust layer subsets via DP synthetic data and perturbation-matched evaluation.
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GeoMark decouples local watermark triggering from centralized ownership attribution using geometry-separated anchors and adaptive neighborhoods to improve robustness against paraphrasing, dimension changes, and clustering attacks while preserving utility.
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
citing papers explorer
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DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
DP-SelFT improves the privacy-utility trade-off for LLM fine-tuning by selecting robust layer subsets via DP synthetic data and perturbation-matched evaluation.
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Geometry-Aware Localized Watermarking for Copyright Protection in Embedding-as-a-Service
GeoMark decouples local watermark triggering from centralized ownership attribution using geometry-separated anchors and adaptive neighborhoods to improve robustness against paraphrasing, dimension changes, and clustering attacks while preserving utility.
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Towards Migrating Neural Network Implementations
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.