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Found 9 papers, 3 papers with code
SEMA: Text Simplification Evaluation through Semantic Alignment
Text simplification is an important branch of natural language processing.
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
Improve bone age assessment by learning from anatomical local regions
In the clinical practice, Tanner and Whitehouse (TW2) method is a widely-used method for radiologists to perform BAA.
FocalMix: Semi-Supervised Learning for 3D Medical Image Detection
Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine.
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks
Then, we formulate the adversarial learning of the generator and discriminator networks as a min-max game.
Orthogonal and Idempotent Transformations for Learning Deep Neural Networks
Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training.
