- Evaluating Robustness to Input Perturbations for Neural Machine ...🔍
- Evaluating the Robustness of Neural Language Models to Input ...🔍
- Evaluating the Robustness of Convolutional Neural Networks ...🔍
- Evaluating Robustness Against Adversarial Attacks🔍
- On the Formal Evaluation of the Robustness of Neural Networks and ...🔍
- Evaluating Robustness of Neural Networks [Virtual]🔍
- Interpreting and Evaluating Neural Network Robustness🔍
- sockeye/README.md at main🔍
Evaluating robustness to input perturbations for neural machine ...
Evaluating Robustness to Input Perturbations for Neural Machine ...
Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics ...
Evaluating Robustness to Input Perturbations for Neural Machine ...
This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input.
Evaluating Robustness to Input Perturbations for Neural Machine ...
This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input and ...
Evaluating the Robustness of Neural Language Models to Input ...
The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced.
Evaluating Robustness to Input Perturbations for Neural Machine ...
With the meaning-preserving restriction, the researchers try to add perturbations on the source inputs as small as possible to ensure the meaning of the source ...
Evaluating Robustness to Input Perturbations for Neural Machine ...
Evaluating Robustness to Input Perturbations for Neural Machine Translation. Xing Niu, Prashant Mathur, Georgiana Dinu, Yaser Al-Onaizan. Amazon AI. Page 2. • ...
Evaluating Robustness to Input Perturbations for Neural Machine ...
Evaluating Robustness to Input Perturbations for Neural Machine Translation. Xing Niu, Prashant Mathur, Georgiana Dinu, Yaser Al-Onaizan. Abstract Paper Share.
Evaluating the Robustness of Convolutional Neural Networks ...
A robust model should be able to generalize well and provide reliable predictions even when dealing with unforeseen inputs or circumstances. The ...
Evaluating Robustness Against Adversarial Attacks - IEEE Xplore
As shown in adversarial attack, by making perturbations on original input, intentionally designed samples may make the model confused ...
Evaluating the Robustness of Neural Language Models to Input ...
This study designs and implements various types of character-level and word-level perturbation methods to simulate realistic scenarios in which input texts ...
Evaluating Robustness to Input Perturbations for Neural Machine ...
Table 3: BLEU, robustness (in percentage), and consistency scores of different subword segmentation methods on original and perturbed test sets.
On the Formal Evaluation of the Robustness of Neural Networks and ...
In the past decade, artificial intelligence (AI), machine learning and particularly deep learning, have achieved unprecedented levels of ...
Evaluating Robustness of Neural Networks [Virtual] - YouTube
... input perturbations, including the first robustness score CLEVER ... machine learning, optimization and reinforcement learning, with ...
Interpreting and Evaluating Neural Network Robustness - IJCAI
As adversarial attacks leverage perturbations in inputs, we switch the loss visualization from its original parameter space into the in- put space and ...
sockeye/README.md at main - GitHub
"Evaluating Robustness to Input Perturbations for Neural Machine Translation". arXiv preprint arXiv:2005.00580 (2020); Niu, Xing, Marine Carpuat ...
Metrics and methods for robustness evaluation of neural networks ...
Recently, especially in computer vision, researchers discovered “natural” perturbations, such as rotations, changes of brightness, or more high- ...
evaluating robustness of neural networks - Research
Neural networks trained only to optimize for training accuracy can often be fooled by adversarial examples — slightly perturbed inputs misclassified with ...
How Many Perturbations Break This Model? Evaluating Robustness ...
We show that sparsity provides valuable insight into neural networks in multiple ways: for instance, it illustrates important differences between current state- ...
Adversarial robustness improvement for deep neural networks
It is well known that DNNs are vulnerable to adversarial examples, i.e. minimal and usually imperceptible perturbations, applied to their inputs ...
Towards Evaluating the Robustness of Neural Networks - YouTube
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural ... Perturbations against Deep Neural ...