5 citations · 11 across the 5 of their papers we have counts for
5 papers
Fast Few-shot Debugging for NLU Test Suites
Christopher Malon, Kai Li, Erik Kruus
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. Given a f…
SplitBrain: Hybrid Data and Model Parallel Deep Learning
Farley Lai, Asim Kadav, Erik Kruus
The recent success of deep learning applications has coincided with those widely available powerful computational resources for training sophisticated machine learning models with…
Improving Neural Network Robustness through Neighborhood Preserving Layers
Bingyuan Liu, Christopher Malon, Lingzhou Xue +1
Robustness against adversarial attack in neural networks is an important research topic in the machine learning community. We observe one major source of vulnerability of neural ne…
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Yao Li, Martin Renqiang Min, Wenchao Yu +3
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of i…
ASAP: Asynchronous Approximate Data-Parallel Computation
Asim Kadav, Erik Kruus
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. Thes…