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Found 8 papers, 1 papers with code
ASAP: Asynchronous Approximate Data-Parallel Computation
Emerging workloads, such as graph processing and machine learning are approximate because of the scale of data involved and the stochastic nature of the underlying algorithms.
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Defending Against Adversarial Examples by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Towards Robustness of Deep Neural Networks via Regularization
Recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples.
Improving Neural Network Robustness through Neighborhood Preserving Layers
Finally, we empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.
SplitBrain: Hybrid Data and Model Parallel Deep Learning
The recent success of deep learning applications has coincided with those widely available powerful computational resources for training sophisticated machine learning models with huge datasets.
Fast Few-shot Debugging for NLU Test Suites
We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem.
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning
Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
