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Found 8 papers, 6 papers with code
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality.
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection
Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks.
Proximity-Informed Calibration for Deep Neural Networks
We examine the problem over 504 pretrained ImageNet models and observe that: 1) Proximity bias exists across a wide variety of model architectures and sizes; 2) Transformer-based models are relatively more susceptible to proximity bias than CNN-based models; 3) Proximity bias persists even after performing popular calibration algorithms like temperature scaling; 4) Models tend to overfit more heavily on low proximity samples than on high proximity samples.
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
To overcome this incoherence issue, we design a \emph{neighborhood agreement measure} between latent spaces and find that this agreement is surprisingly well-correlated with the reliability of a model's predictions.
Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
Trustworthy machine learning is of primary importance to the practical deployment of deep learning models.
Probabilistic Knowledge Distillation of Face Ensembles
Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
How do we know when the predictions made by a classifier can be trusted?
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Given high-dimensional time series data (e. g., sensor data), how can we detect anomalous events, such as system faults and attacks?
