Search Results for author:
Found 8 papers, 6 papers with code
Machine Translation Testing via Syntactic Tree Pruning
The results show that STP can accurately find 5, 073 unique erroneous translations in Google Translate and 5, 100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64. 5% and 65. 4% precision, respectively.
DREAM: Debugging and Repairing AutoML Pipelines
In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task.
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
The common practice leverages incremental learning (IL), e. g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data.
UNICORN: A Unified Backdoor Trigger Inversion Framework
Then, it proposes a unified framework to invert backdoor triggers based on the formalization of triggers and the identified inner behaviors of backdoor models from our analysis.
Rethinking the Reverse-engineering of Trojan Triggers
On average, the detection accuracy of our method is 93\%.
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning
Existing attacks use visible patterns (e. g., a patch or image transformations) as triggers, which are vulnerable to human inspection.
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level.
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
By further analyzing the training process and model architectures, we found that piece-wise linear functions cause this hyperplane surface.
