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Found 5 papers, 2 papers with code
Learning Interpretable Differentiable Logic Networks
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data.
Model-Enhanced LLM-Driven VUI Testing of VPA Apps
To tackle the inherent lack of a visible user interface in the VPA app, two strategies are employed during testing, i. e., chatbot-style testing and model-based testing.
SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning
Recent years have witnessed tremendous success in Self-Supervised Learning (SSL), which has been widely utilized to facilitate various downstream tasks in Computer Vision (CV) and Natural Language Processing (NLP) domains.
CTRL: Clustering Training Losses for Label Error Detection
We propose a novel framework, called CTRL (Clustering TRaining Losses for label error detection), to detect label errors in multi-class datasets.
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain
However, most of the triggers used in the current study are fixed patterns patched on a small fraction of an image and are often clearly mislabeled, which is easily detected by humans or defense methods such as Neural Cleanse and SentiNet.
