9 citations · 10 across the 5 of their papers we have counts for
5 papers
SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
Zikun Liu, Liang Luo, Qianru Li +31
Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance, their computational demands…
SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification
Jingyi Shen, Yuhan Duan, Han-Wei Shen
Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse…
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data
Jingyi Shen, Han-Wei Shen
Although many deep-learning-based super-resolution approaches have been proposed in recent years, because no ground truth is available in the inference stage, few can quantify the…
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
Jingyi Shen, Haoyu Li, Jiayi Xu +2
Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow f…
An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
Jingyi Shen, Han-Wei Shen
Despite the great success of Convolutional Neural Networks (CNNs) in Computer Vision and Natural Language Processing, the working mechanism behind CNNs is still under extensive dis…