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Found 18 papers, 3 papers with code
Robust Learning Protocol for Federated Tumor Segmentation Challenge
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022).
Federated Unlearning: How to Efficiently Erase a Client in FL?
We explore the problem of removing any client's contribution in federated learning (FL).
Challenges and Pitfalls of Bayesian Unlearning
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model.
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
Certified Federated Adversarial Training
We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples.
Automated Robustness with Adversarial Training as a Post-Processing Step
Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging.
The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models
Deep Generative Models (DGMs) are a popular class of deep learning models which find widespread use because of their ability to synthesize data from complex, high-dimensional manifolds.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
FAT: Federated Adversarial Training
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML).
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians.
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Deep Learning models are vulnerable to adversarial examples, i. e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence.
Open-World Visual Recognition Using Knowledge Graphs
First, a knowledge-graph representation is learned to embed a large set of entities into a semantic space.
Efficient Defenses Against Adversarial Attacks
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Generative Adversarial Networks (GANs) have become a widely popular framework for generative modelling of high-dimensional datasets.
