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06/19/2023
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification
Scalability is a significant challenge when it comes to applying differe...
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03/21/2023
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
Lidar is a vital sensor for estimating the depth of a scene. Typical spi...
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03/08/2023
Differential Privacy Meets Neural Network Pruning
A major challenge in applying differential privacy to training deep neur...
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03/03/2023
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
Maximum mean discrepancy (MMD) is a particularly useful distance metric ...
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05/11/2022
Revisiting Random Channel Pruning for Neural Network Compression
Channel (or 3D filter) pruning serves as an effective way to accelerate ...
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11/10/2020
Dirichlet Pruning for Neural Network Compression
We introduce Dirichlet pruning, a novel post-processing technique to tra...
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10/26/2020
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning
We introduce a novel framework to quantify the importance of each input ...
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02/26/2020
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple Practical Synthetic Data Generation
We present a differentially private data generation paradigm using rando...
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07/03/2019
Neuron ranking -- an informed way to condense convolutional neural networks architecture
Convolutional neural networks (CNNs) in recent years have made a dramati...
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02/07/2019
Radial and Directional Posteriors for Bayesian Neural Networks
We propose a new variational family for Bayesian neural networks. We dec...
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09/27/2014
