Neural network inference is the process of using a trained artificial neural network to make predictions or decisions on new, unseen data. After a neural network learns patterns from a dataset, inference applies this learned knowledge to generate outputs. In digital asset applications, this could involve analyzing market trends, detecting fraudulent transactions, or optimizing trading strategies. It represents the practical application phase of machine learning models.
Context
Neural network inference is increasingly relevant in the crypto space for advanced analytics, algorithmic trading, and security applications. Its application in predicting market movements or identifying anomalies in blockchain data is a growing area of interest. News often reports on the integration of artificial intelligence and machine learning into various decentralized platforms and financial services.
zkVC introduces CRPC and PSQ to reduce matrix multiplication constraints from O(n3) to O(n), achieving over 12x faster ZK proof generation for verifiable AI.
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