8 citations · 15 across the 11 of their papers we have counts for
7 papers · 1 filter
One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression
Mikołaj Janusz, Tomasz Wojnar, Yawei Li +2
Pruning is a core technique for compressing neural networks to improve computational efficiency. This process is typically approached in two ways: one-shot pruning, which involves…
Shapley Pruning for Neural Network Compression
Kamil Adamczewski, Yawei Li, Luc van Gool
Neural network pruning is a rich field with a variety of approaches. In this work, we propose to connect the existing pruning concepts such as leave-one-out pruning and oracle prun…
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
Kamil Adamczewski, Christos Sakaridis, Vaishakh Patil +1
Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases…
Differential Privacy Meets Neural Network Pruning
Kamil Adamczewski, Mijung Park
A major challenge in applying differential privacy to training deep neural network models is scalability.The widely-used training algorithm, differentially private stochastic gradi…
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Yilin Yang, Kamil Adamczewski, Danica J. Sutherland +2
Maximum mean discrepancy (MMD) is a particularly useful distance metric for differentially private data generation: when used with finite-dimensional features it allows us to summa…
Dirichlet Pruning for Neural Network Compression
Kamil Adamczewski, Mijung Park
We introduce Dirichlet pruning, a novel post-processing technique to transform a large neural network model into a compressed one. Dirichlet pruning is a form of structured pruning…