4 citations · 7 across the 4 of their papers we have counts for
4 papers
A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models
Ying Lu, Peng-Fei Zhou, Qi-Xuan Fang +3
Dense linear maps carry much of the parameter and computational burden of modern neural networks, yet their dense form leaves the organization of learned couplings implicit. Quantu…
Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation
Yingjing Lu, Runde Yang
Self-explaining models are models that reveal decision making parameters in an interpretable manner so that the model reasoning process can be directly understood by human beings.…
The Level Weighted Structural Similarity Loss: A Step Away from the MSE
Yingjing Lu
The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, t…
Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss
Yingjing Lu
Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been crea…