6 citations · 16 across the 6 of their papers we have counts for
6 papers
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions
Junyang Cai, Khai-Nguyen Nguyen, Nishant Shrestha +5
One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter…
Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li, Jeff M. Phillips, Xin Yu +2
Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for the…
Meta Knowledge Condensation for Federated Learning
Ping Liu, Xin Yu, Joey Tianyi Zhou
Existing federated learning paradigms usually extensively exchange distributed models at a central solver to achieve a more powerful model. However, this would incur severe communi…
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Zhimin Li, Shusen Liu, Xin Yu +5
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks have been…
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Aidan Good, Jiaqi Lin, Hannah Sieg +5
Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that t…
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu, Thiago Serra, Srikumar Ramalingam +1
Neural networks tend to achieve better accuracy with training if they are larger -- even if the resulting models are overparameterized. Nevertheless, carefully removing such excess…