Page 13: Research news on Trustworthy machine learning

Trustworthy machine learning addresses methods for training and deploying models that are secure, privacy-preserving, and robust to manipulation. Work in this area develops federated and decentralized learning schemes, cryptographic and homomorphic encryption frameworks, and privacy-preserving compression to protect data and models. It also studies adversarial example generation and defenses, certified unlearning, bias and spurious correlation mitigation, and the use of synthetic and filtered data. Applications span fraud and cyberattack detection, fake news and deception detection, and secure automation systems.

Computer Sciences

BAFT AI autosave system can cut training losses by 98%

A research collaboration between Shanghai Jiao Tong University, Shanghai Qi Zhi Institution, and Huawei Technologies has introduced BAFT, a cutting-edge autosave system for AI training that minimizes downtime and optimizes ...

Computer Sciences

Neural network learns to hesitate for improved accuracy

Researchers from the Skoltech AI Center, together with colleagues from the Institute for Information Transmission Problems of the Russian Academy of Sciences, have developed a method that allows neural networks to more accurately ...

Machine learning & AI

This AI model is more certain about uncertainty

Artificial intelligence (AI) plays a role in virtually every aspect of our lives, from self-driving cars to smart vacuum cleaners, to computer models that can predict the course of an epidemic. No matter how advanced these ...

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