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cs.LG2024
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
Jing Zhang, Shuzhen Sun, Peng Zhang +5
Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resourc…
cs.LG2024★ 2 cited
Quantum Implicit Neural Representations
Jiaming Zhao, Wenbo Qiao, Peng Zhang +1
Implicit neural representations have emerged as a powerful paradigm to represent signals such as images and sounds. This approach aims to utilize neural networks to parameterize th…