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02/25/2022
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Federated Learning (FL) is a novel paradigm for the shared training of m...
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12/20/2021
Certified Federated Adversarial Training
In federated learning (FL), robust aggregation schemes have been develop...
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09/06/2021
Automated Robustness with Adversarial Training as a Post-Processing Step
Adversarial training is a computationally expensive task and hence searc...
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08/31/2021
DLPFS: The Data Leakage Prevention FileSystem
Shared folders are still a common practice for granting third parties ac...
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08/03/2021
The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks
Deep Generative Models (DGMs) allow users to synthesize data from comple...
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12/03/2020
FAT: Federated Adversarial Training
Federated learning (FL) is one of the most important paradigms addressin...
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07/22/2020
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning witho...
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05/09/2019
Exploring the Hyperparameter Landscape of Adversarial Robustness
Adversarial training shows promise as an approach for training models th...
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11/20/2018
Castor: Contextual IoT Time Series Data and Model Management at Scale
We demonstrate Castor, a cloud-based system for contextual IoT time seri...
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07/03/2018
Adversarial Robustness Toolbox v0.2.2
Adversarial examples have become an indisputable threat to the security ...
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06/15/2018
Automated Image Data Preprocessing with Deep Reinforcement Learning
Data preparation, i.e. the process of transforming raw data into a forma...
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02/10/2018
Learning Correlation Space for Time Series
We propose an approximation algorithm for efficient correlation search i...
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01/16/2018
Learning Features For Relational Data
Feature engineering is one of the most important but tedious tasks in da...
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06/01/2017
One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming task...
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05/25/2017
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Generative Adversarial Networks (GANs) have become a widely popular fram...
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05/19/2015
Multi-task additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which r...
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03/15/2012
