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README.md
README.md
 
 
[001][Dynamic Sparsity][Network Regrowth] Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.md
[001][Dynamic Sparsity][Network Regrowth] Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.md
 
 
[002][Dynamic Sparsity][Network Regrowth] Rigging the Lottery: Making All Tickets Winners.md
[002][Dynamic Sparsity][Network Regrowth] Rigging the Lottery: Making All Tickets Winners.md
 
 
[003][Robustness and Efficiency][Approximation and Robustness] Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks.md
[003][Robustness and Efficiency][Approximation and Robustness] Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks.md
 
 
[004][Robustness and Efficiency][Approximation and Robustness] Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks.md
[004][Robustness and Efficiency][Approximation and Robustness] Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks.md
 
 
[005][Robustness and Efficiency][Approximation and Robustness] Defensive approximation- securing CNNs using approximate computing.md
[005][Robustness and Efficiency][Approximation and Robustness] Defensive approximation- securing CNNs using approximate computing.md
 
 
[006][Robustness and Efficiency][Approximation and Robustness] Adversarial Robustness via Random Projection Filters.md
[006][Robustness and Efficiency][Approximation and Robustness] Adversarial Robustness via Random Projection Filters.md
 
 
[007][Robustness and Efficiency][Quantization and Robustness] Defensive Quantization: When Efficiency Meets Robustness.md
[007][Robustness and Efficiency][Quantization and Robustness] Defensive Quantization: When Efficiency Meets Robustness.md
 
 
[008][Robustness and Efficiency][Quantization and Robustness] Empir: Ensembles of mixed precision deep networks for increased robustness against adversarial attacks.md
[008][Robustness and Efficiency][Quantization and Robustness] Empir: Ensembles of mixed precision deep networks for increased robustness against adversarial attacks.md
 
 
[009][Robustness and Efficiency][Quantization and Robustness] 2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency.md
[009][Robustness and Efficiency][Quantization and Robustness] 2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency.md
 
 
[010][Robustness and Efficiency][Quantization and Robustness] Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference.md
[010][Robustness and Efficiency][Quantization and Robustness] Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference.md
 
 
[011][Robustness and Efficiency][Sparsity and Robustness] Enhancing adversarial defense by k-winners-take-all.md
[011][Robustness and Efficiency][Sparsity and Robustness] Enhancing adversarial defense by k-winners-take-all.md
 
 
[012][Robustness and Efficiency][Sparsity and Robustness] Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks.md
[012][Robustness and Efficiency][Sparsity and Robustness] Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks.md
 
 
[013][Robustness and Efficiency][Sparsity and Robustness] Triple Wins: Boosting Accuracy, Robustness, and Efficiency Together by Enabling Input-Adaptive Inference.md
[013][Robustness and Efficiency][Sparsity and Robustness] Triple Wins: Boosting Accuracy, Robustness, and Efficiency Together by Enabling Input-Adaptive Inference.md
 
 
[014][Robustness and Efficiency][Quantization and Robustness] Attacking Binarized Neural Networks.md
[014][Robustness and Efficiency][Quantization and Robustness] Attacking Binarized Neural Networks.md
 
 
[015][Robustness and Efficiency][Sparsity and Robustness] Robust Binary Models by Pruning Randomly-initialized Networks.md
[015][Robustness and Efficiency][Sparsity and Robustness] Robust Binary Models by Pruning Randomly-initialized Networks.md
 
 
[016][Robustness and Efficiency][Quantization and Robustness] Gradient L1 Regularization For Quantization Robustness.md
[016][Robustness and Efficiency][Quantization and Robustness] Gradient L1 Regularization For Quantization Robustness.md
 
 
[017][Robustness and Efficiency][Quantization and Robustness] Error-Silenced Quantization: Bridging Robustness and Compactness.md
[017][Robustness and Efficiency][Quantization and Robustness] Error-Silenced Quantization: Bridging Robustness and Compactness.md
 
 
[018][Robustness and Efficiency][Sparsity and Robustness] Adversarial Neural Pruning with Latent Vulnerability Suppression.md
[018][Robustness and Efficiency][Sparsity and Robustness] Adversarial Neural Pruning with Latent Vulnerability Suppression.md
 
 
[019][Robustness][Theoretically Analysis] Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack.md
[019][Robustness][Theoretically Analysis] Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack.md
 
 
[020][Robustness Theoretical Analysis] Theoretical evidence for adversarial robustness through randomization.md
[020][Robustness Theoretical Analysis] Theoretical evidence for adversarial robustness through randomization.md
 
 

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