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Found 9 papers, 6 papers with code
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems.
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between.
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring.
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels.
High Accuracy and High Fidelity Extraction of Neural Networks
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
Large-Scale Evolution of Image Classifiers
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Ranked #117 on
Image Classification
on CIFAR-10
