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Found 13 papers, 5 papers with code
Task-Agnostic Detector for Insertion-Based Backdoor Attacks
Textual backdoor attacks pose significant security threats.
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models.
Learning to Abstain From Uninformative Data
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high.
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution.
A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases.
A Study of the Attention Abnormality in Trojaned BERTs
Trojan attacks raise serious security concerns.
Learning to Abstain in the Presence of Uninformative Data
Learning and decision making in domains with naturally high noise-to-signal ratios – such as Finance or Public Health – can be challenging and yet extremely important.
Topological Detection of Trojaned Neural Networks
Deep neural networks are known to have security issues.
Learning with Feature-Dependent Label Noise: A Progressive Approach
Label noise is frequently observed in real-world large-scale datasets.
Ranked #12 on
Learning with noisy labels
on ANIMAL
A Topological Filter for Learning with Label Noise
Noisy labels can impair the performance of deep neural networks.
Error-Bounded Correction of Noisy Labels
To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.
Ranked #40 on
Image Classification
on Clothing1M
Label Cleaning with Likelihood Ratio Test
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
