Kamil Adamczewski

According to our database1, Kamil Adamczewski authored at least 19 papers between 2014 and 2024.

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Bibliography

2024
Scaling Laws for Fine-Grained Mixture of Experts.
CoRR, 2024

2023
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification.
CoRR, 2023

Differential Privacy Meets Neural Network Pruning.
CoRR, 2023

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.
CoRR, 2023

2022
Hermite Polynomial Features for Private Data Generation.
Proceedings of the International Conference on Machine Learning, 2022

Revisiting Random Channel Pruning for Neural Network Compression.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion.
Proceedings of the Conference on Robot Learning, 2022

2021
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Dirichlet Pruning for Convolutional Neural Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Dirichlet Pruning for Neural Network Compression.
CoRR, 2020

Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning.
CoRR, 2020

Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation.
CoRR, 2020

Radial and Directional Posteriors for Bayesian Deep Learning.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Neuron ranking - an informed way to condense convolutional neural networks architecture.
CoRR, 2019

Radial and Directional Posteriors for Bayesian Neural Networks.
CoRR, 2019

2015
The Smoothed Pólya-Vinogradov Inequality.
Integers, 2015

Discrete Tabu Search for Graph Matching.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Subgraph matching using compactness prior for robust feature correspondence.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
How good is the Shapley value-based approach to the influence maximization problem?
Proceedings of the ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic, 2014


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