Research map / Federated learning and privacy / Privacy attacks and secure learning
ReLUPruner: Rethinking ReLU Importance with Taylor Expansion for Efficient Private Inference
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- CipherPrune: Efficient and Scalable Private Transformer Inference (ICLR 2025 · similarity 0.85)
- ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models (ICLR 2024 · similarity 0.85)
- Disparate Impact on Group Accuracy of Linearization for Private Inference (ICML 2024 · similarity 0.84)
- United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus (AAAI 2024 · similarity 0.84)
- Revisiting Differentially Private ReLU Regression (NeurIPS 2024 · similarity 0.83)
- Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy (NeurIPS 2025 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.