Search Results for author:
Found 3 papers, 2 papers with code
zkLLM: Zero Knowledge Proofs for Large Language Models
The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe.
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution
Our approach effectively addresses the challenges associated with degradation estimation and correction errors, paving the way for more accurate blind image SR.
zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training
In response to this challenge, we present zero-knowledge deep learning (zkDL), an efficient zero-knowledge proof for deep learning training.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
