304 citations · 437 across the 8 of their papers we have counts for
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cs.NE2023★ 2 cited
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
Peng Zhou, Alexander J. Edwards, Frederick B. Mancoff +3
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approach…
quant-ph2023
NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
Yangjun Wu, Chu Guo, Yi Fan +2
Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampl…
cs.LG2023★ 5 cited
Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
Yong Qing, Ke Li, Peng-Fei Zhou +1
Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if…