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Found 10 papers, 3 papers with code
Efficient Membership Inference Attacks by Bayesian Neural Network
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model.
On the Noise Robustness of In-Context Learning for Text Generation
In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning.
Similarity-Navigated Conformal Prediction for Graph Neural Networks
In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage.
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
In this paper, we propose a straightforward, novel, and training-free hardness score named Distorting-based Learning Complexity (DLC), to identify informative images and instructions from the downstream dataset efficiently.
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world.
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation
Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches.
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models
With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-based post-hoc XAI models and explanations are socially misaligned.
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
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Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants
Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently.
