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Found 11 papers, 5 papers with code
Variance Reduction in Deep Learning: More Momentum is All You Need
Variance reduction (VR) techniques have contributed significantly to accelerating learning with massive datasets in the smooth and strongly convex setting (Schmidt et al., 2017; Johnson & Zhang, 2013; Roux et al., 2012).
Continental-Scale Building Detection from High Resolution Satellite Imagery
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously.
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Unfortunately, once the models are sold they can be easily copied and redistributed.
Fooling End-to-end Speaker Verification by Adversarial Examples
We also present two black-box attacks: where the adversarial examples were generated with a system that was trained on YOHO, but the attack is on a system that was trained on NTIMIT; and when the adversarial examples were generated with a system that was trained on Mel-spectrum feature set, but the attack is on a system that was trained on MFCC.
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases
ConvNets and Imagenet have driven the recent success of deep learning for image classification.
Unbounded cache model for online language modeling with open vocabulary
Recently, continuous cache models were proposed as extensions to recurrent neural network language models, to adapt their predictions to local changes in the data distribution.
Countering Adversarial Images using Input Transformations
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.
mixup: Beyond Empirical Risk Minimization
We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Houdini: Fooling Deep Structured Prediction Models
Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines.
Parseval Networks: Improving Robustness to Adversarial Examples
We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1.
