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Found 10 papers, 6 papers with code
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
This resolves the aforementioned disregard for target areas and redundancy.
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models
In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial.
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples.
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF).
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted.
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks
Our experiments demonstrate that FewSOME performs at state-of-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on.
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane).
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets
Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data.
Improving Explainable Recommendations with Synthetic Reviews
We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews.
Automatic Generation of Natural Language Explanations
The model generates text reviews given a combination of the review and ratings score that express opinions about different factors or aspects of an item.
