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Found 13 papers, 8 papers with code
Transformers4NewsRec: A Transformer-based News Recommendation Framework
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations.
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality.
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
Currently, radiologists grade the severity of OA on an ordinal scale from zero to four using the Kellgren-Lawrence (KL) system.
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
This resolves the aforementioned disregard for target areas and redundancy.
RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models
The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.
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.
