awesome-neural-reprogramming-prompting
github.com/huckiyang/awesome-neural-reprogramming-prompting ↗A curated list of awesome adversarial reprogramming and input prompting methods for neural networks since 2022
40
GitHub Stars
29
Curated Resources
4
Categories
2 hours ago
Last Refreshed
Neural Adapters in Decoder, Encoder, and InputsNeural Reprogramming or Adversarial Reprogramming or Offsite-TuningInput-Level Neural Model Prompting for Vision and SpeechTheory
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me neural reprogramming or adversarial reprogramming or offsite-tuning resources from awesome-neural-reprogramming-prompting"
Installation instructions →What's inside
Neural Reprogramming or Adversarial Reprogramming or Offsite-Tuning
- Adversarial reprogramming of neural networks
F. Elsayed et al.
- Adversarial Reprogramming of Text Classification Neural Networks
P. Neekhara et al.
- Adversarial Reprogramming Revisited
M. Englert et al.
- A Study of Low-Resource Speech Commands Recognition based on Adversarial Reprogramming
H Yen et al.
- Cross-modal Adversarial Reprogramming
P. Neekhara et al.
- Fairness Reprogramming
G. Zhang et al.
Input-Level Neural Model Prompting for Vision and Speech
- An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks
K.-W. Chang et al.
- AudioLM: a Language Modeling Approach to Audio Generation
Z. Borsos et al.
- SPEECHPROMPT V2: PROMPT TUNING FOR SPEECH CLASSIFICATION TASKS
K.-W. Chang et al.
- Understanding and Improving Visual Prompting: A Label-Mapping Perspective
A. Chen et al.
- Visual Prompting: Modifying Pixel Space to Adapt Pre-trained Models
H. Bahng et al.
- WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models
H. Gao et al.
Neural Adapters in Decoder, Encoder, and Inputs
- A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model
S. Radhakrishnan et al.
- Differentially Private Adapters for Parameter Efficient Acoustic Modeling
C.-W. Ho et al.
- Parameter-Efficient Learning for Text-to-Speech Accent Adaptation
L.-J. Yang et al.
Theory
Showing a sample of 29 resources. View the full list on GitHub →