Kamil Adamczewski
Kamil Adamczewski
Max Planck Institute for Intelligent Systems
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Revisiting random channel pruning for neural network compression
Y Li, K Adamczewski, W Li, S Gu, R Timofte, L Van Gool
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
F Harder, K Adamczewski, M Park
International conference on artificial intelligence and statistics, 1819-1827, 2021
Discrete tabu search for graph matching
K Adamczewski, Y Suh, K Mu Lee
Proceedings of the IEEE international conference on computer vision, 109-117, 2015
Subgraph matching using compactness prior for robust feature correspondence
Y Suh, K Adamczewski, K Mu Lee
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
Radial and directional posteriors for bayesian deep learning
C Oh, K Adamczewski, M Park
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5298-5305, 2020
Hermite polynomial features for private data generation
M Vinaroz, MA Charusaie, F Harder, K Adamczewski, MJ Park
International Conference on Machine Learning, 22300-22324, 2022
How good is the Shapley value-based approach to the influence maximization problem?
K Adamczewski, S Matejczyk, TP Michalak
arXiv preprint arXiv:1409.7830, 2014
Dirichlet pruning for convolutional neural networks
K Adamczewski, M Park
International Conference on Artificial Intelligence and Statistics, 3637-3645, 2021
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Y Yang, K Adamczewski, DJ Sutherland, X Li, M Park
arXiv preprint arXiv:2303.01687, 2023
Differential privacy meets neural network pruning
K Adamczewski, M Park
arXiv preprint arXiv:2303.04612, 2023
Bayesian importance of features (bif)
K Adamczewski, F Harder, M Park
arXiv preprint arXiv:2010.13872, 2020
The Smoothed Pólya-Vinogradov Inequality.
K Adamczewski, E Trevino
Integers 15, A20, 2015
Neuron ranking--an informed way to condense convolutional neural networks architecture
K Adamczewski, M Park
arXiv preprint arXiv:1907.02519, 2019
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification
K Adamczewski, Y He, M Park
arXiv preprint arXiv:2306.11754, 2023
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
K Adamczewski, C Sakaridis, V Patil, L Van Gool
Conference on Robot Learning, 561-570, 2023
Less is More: Discovering Redundancies in Neural Networks and Data for Improved Efficiency, Intrerpretability and Privacy
K Adamczewski
ETH Zurich, 2023
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning
K Adamczewski, F Harder, M Park
stat 1050, 26, 2020
Neuron ranking-an informed way to compress convolutional neural networks
K Adamczewski, M Park
Department of Mathematics, Dartmouth College, Hanover, New Hampshire kamil. m. adamczewski@ gmail. com Enrique Trevino Department of Mathematics and Computer Science, Lake …
K Adamczewski
INTEGERS 15, 2, 2015
Graph Matching using Discrete Tabu Search on the Penalized Association Graph
K Adamczewski
서울대학교 대학원, 2015
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