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11/23/2021
Variance Reduction in Deep Learning: More Momentum is All You Need
Variance reduction (VR) techniques have contributed significantly to acc...
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07/26/2021
Continental-Scale Building Detection from High Resolution Satellite Imagery
Identifying the locations and footprints of buildings is vital for many ...
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06/24/2020
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensur...
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06/10/2020
On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
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02/13/2018
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Deep Neural Networks have recently gained lots of success after enabling...
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01/10/2018
Fooling End-to-end Speaker Verification by Adversarial Examples
Automatic speaker verification systems are increasingly used as the prim...
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11/30/2017
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism
ConvNets and Imagenet have driven the recent success of deep learning fo...
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11/07/2017
Unbounded cache model for online language modeling with open vocabulary
Recently, continuous cache models were proposed as extensions to recurre...
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10/31/2017
Countering Adversarial Images using Input Transformations
This paper investigates strategies that defend against adversarial-examp...
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10/25/2017
mixup: Beyond Empirical Risk Minimization
Large deep neural networks are powerful, but exhibit undesirable behavio...
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07/17/2017
Houdini: Fooling Deep Structured Prediction Models
Generating adversarial examples is a critical step for evaluating and im...
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04/28/2017
Parseval Networks: Improving Robustness to Adversarial Examples
We introduce Parseval networks, a form of deep neural networks in which ...
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09/14/2016
Efficient softmax approximation for GPUs
We propose an approximate strategy to efficiently train neural network b...
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09/18/2010
