arXiv (All)
open access
On-the-go Forgetting without Explicit Unlearning via ERASE
Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. In this work, we introduce ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forget
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders#1105614
- SongCraft: Unified Song Generation and Editing with Reconstructive Learning#1110680
- Towards Effective Black-Box Adversarial Attacks on Deep Code Models via Structural and Identifier Perturbations#1062134
- Don't Waste the Noise: Importance-Guided Perturbation Allocation under Joint Global and Local Constraints#1155796
- RAMP: Reversing Adversarial Perturbations to Strengthen Clean-Label Backdoor Attacks against Malware Detectors#1063633
Record · ID 973580
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive