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Improving Low Resource Code-switched ASR using Augmented Code-switched TTS
Building Automatic Speech Recognition (ASR) systems for code-switched sp...
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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
We construct an unsupervised learning model that achieves nonlinear dise...
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S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropr...
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Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects is a crucial prerequisite for r...
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Devising Malware Characterstics using Transformers
With the increasing number of cybersecurity threats, it becomes more dif...
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On the Effectiveness of Low Frequency Perturbations
Carefully crafted, often imperceptible, adversarial perturbations have b...
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Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training
We propose Max-Margin Adversarial (MMA) training for directly maximizing...
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CAAD 2018: Generating Transferable Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...
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GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Deep neural networks (DNNs) are vulnerable to adversarial examples, even...
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Generating Natural Language Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...
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Bypassing Feature Squeezing by Increasing Adversary Strength
Feature Squeezing is a recently proposed defense method which reduces th...
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Attacking the Madry Defense Model with L_1-based Adversarial Examples
The Madry Lab recently hosted a competition designed to test the robustn...
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EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural network...
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ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Deep neural networks (DNNs) are one of the most prominent technologies o...
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