
Skew Orthogonal Convolutions
Training convolutional neural networks with a Lipschitz constraint under...
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Understanding Overparameterization in Generative Adversarial Networks
A broad class of unsupervised deep learning methods such as Generative A...
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Improved, Deterministic Smoothing for L1 Certified Robustness
Randomized smoothing is a general technique for computing sampledepende...
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Low Curvature Activations Reduce Overfitting in Adversarial Training
Adversarial training is one of the most effective defenses against adver...
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Benchmarking Deep Learning Interpretability in Time Series Predictions
Saliency methods are used extensively to highlight the importance of inp...
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Tight SecondOrder Certificates for Randomized Smoothing
Randomized smoothing is a popular way of providing robustness guarantees...
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Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Optimal Transport (OT) distances such as Wasserstein have been used in s...
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Winning Lottery Tickets in Deep Generative Models
The lottery ticket hypothesis suggests that sparse, subnetworks of a gi...
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GANs with Variational Entropy Regularizers: Applications in Mitigating the ModeCollapse Issue
Building on the success of deep learning, Generative Adversarial Network...
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Certifying Confidence via Randomized Smoothing
Randomized smoothing has been shown to provide good certifiedrobustness...
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Dual Manifold Adversarial Robustness: Defense against Lp and nonLp Adversarial Attacks
Adversarial training is a popular defense strategy against attack threat...
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Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
Adversarial poisoning attacks distort training data in order to corrupt ...
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Influence Functions in Deep Learning Are Fragile
Influence functions approximate the effect of training samples in testt...
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Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
We present adversarial attacks and defenses for the perceptual adversari...
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Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Deep neural networks are being increasingly used in real world applicati...
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SecondOrder Provable Defenses against Adversarial Attacks
A robustness certificate is the minimum distance of a given input to the...
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Subadditivity of Probability Divergences on BayesNets with Applications to Time Series GANs
GANs for time series data often use sliding windows or selfattention to...
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(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Patch adversarial attacks on images, in which the attacker can distort p...
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Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Randomized smoothing, using just a simple isotropic Gaussian distributio...
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Playing it Safe: Adversarial Robustness with an Abstain Option
We explore adversarial robustness in the setting in which it is acceptab...
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Bounding Singular Values of Convolution Layers
In deep neural networks, the spectral norm of the Jacobian of a layer bo...
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Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation
Recently, techniques have been developed to provably guarantee the robus...
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Adversarial Robustness of FlowBased Generative Models
Flowbased generative models leverage invertible generator functions to ...
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SecondOrder Group Influence Functions for BlackBox Predictions
With the rapid adoption of machine learning systems in sensitive applica...
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Quantum Wasserstein Generative Adversarial Networks
The study of quantum generative models is wellmotivated, not only becau...
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InputCell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
Recent efforts to improve the interpretability of deep neural networks u...
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Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
In the last couple of years, several adversarial attack methods based on...
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Strong Baseline Defenses Against CleanLabel Poisoning Attacks
Targeted cleanlabel poisoning is a type of adversarial attack on machin...
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Interpretable Adversarial Training for Text
Generating highquality and interpretable adversarial examples in the te...
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Functional Adversarial Attacks
We propose functional adversarial attacks, a novel class of threat model...
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Certifiably Robust Interpretation in Deep Learning
Although gradientbased saliency maps are popular methods for deep learn...
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Adversarially Robust Distillation
Knowledge distillation is effective for producing small highperformance...
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Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
Understanding proper distance measures between distributions is at the c...
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Understanding Impacts of HighOrder Loss Approximations and Features in Deep Learning Interpretation
Current methods to interpret deep learning models by generating saliency...
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Robustness Certificates Against Adversarial Examples for ReLU Networks
While neural networks have achieved high performance in different learni...
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Compressing GANs using Knowledge Distillation
Generative Adversarial Networks (GANs) have been used in several machine...
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Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Building on the success of deep learning, two modern approaches to learn...
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Are adversarial examples inevitable?
A wide range of defenses have been proposed to harden neural networks ag...
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Understanding GANs: the LQG Setting
Generative Adversarial Networks (GANs) have become a popular method to l...
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Porcupine Neural Networks: (Almost) All Local Optima are Global
Neural networks have been used prominently in several machine learning a...
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Maximally Correlated Principal Component Analysis
In the era of big data, reducing data dimensionality is critical in many...
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Network Maximal Correlation
We introduce Network Maximal Correlation (NMC) as a multivariate measure...
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Maximum Likelihood Latent Space Embedding of Logistic Random Dot Product Graphs
A latent space model for a family of random graphs assigns realvalued v...
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Soheil Feizi
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