awesome-machine-learning-reliability
github.com/zbchern/awesome-machine-learning-reliability ↗A curated list of awesome resources regarding machine learning reliability.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me security resources from awesome-machine-learning-reliability"
Installation instructions →What's inside
Conferences
- ACM Conference on Computer and Communications Security (CCS)Security
- Annual Conference on Neural Information Processing Systems (NeurIPS)Machine Learning
- Annual Meeting of the Association for Computational Linguistics (ACL)Natural Language Processing
- Conference on Empirical Methods in Natural Language Processing (EMNLP)Natural Language Processing
- IEEE Symposium on Security and Privacy (S&P)Security
- International Conference on Learning Representations (ICLR)Machine Learning
Adversarial NLP and Speech
- Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer.
- Adversarial Examples for Evaluating Reading Comprehension Systems
Robin Jia and Percy Liang.
- Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato, Andrew M. Dai, and Ian Goodfellow.
- Crafting Adversarial Input Sequences for Recurrent Neural Networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang.
- Did the Model Understand the Question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere.
- Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang.
Survey
- Adversarial Examples - A Complete Characterisation of the Phenomenon
Alexandru Constantin Serban and Erik Poll.
- Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li.
- Adversarial Examples: Opportunities and Challenges
Jiliang Zhang and Chen Li.
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar and Ajmal Mian.
Papers
- Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection MethodsDefense
Nicholas Carlini and David Wagner.
- Adversarial Examples in the Physical WorldAttack
Alexey Kurakin, Ian Goodfellow, and Samy Bengio.
- Adversarial Logit PairingDefense
Harini Kannan, Alexey Kurakin, and Ian Goodfellow.
- Attacking Binarized Neural NetworksDefense
Angus Galloway, Graham W. Taylor, and Medhat Moussa.
- Attacks Meet Interpretability: Attribute-steered Detection of Adversarial SamplesDefense
Guanhong Tao, Shiqing Ma, Yingqi Liu, and Xiangyu Zhang.
- Benchmarking Neural Network Robustness to Common Corruptions and PerturbationsAttack
Dan Hendrycks and Thomas Dietterich.
Blogs
Other Applications
- Black-Box Attacks against RNN based Malware Detection Algorithms
Weiwei Hu, Ying Tan
Provable and Verifiable AI Robustness
- Certified Defenses against Adversarial Examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang.
- Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman, Timon Gehr, and Martin Vechev.
- On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli.
- Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J. Zico Kolter.
- Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
Kai Y. Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry.
Machine Learning Testing
- DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang.
- DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray.
- DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana.
- Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu.
- MODE: Automated Neural Network Model Debugging via State Differential Analysis and Input Selection
Shiqing Ma, Yingqi Liu, Wen-Chuan Lee, Xiangyu Zhang, Ananth Grama.
- Testing Untestable Neural Machine Translation: An Industrial Case
Wujie Zheng, Wenyu Wang, Dian Liu, Changrong Zhang, Qinsong Zeng, Yuetang Deng, Wei Yang, Pinjia He, Tao Xie.
Showing a sample of 78 resources. View the full list on GitHub →