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Found 7 papers, 0 papers with code
Dynamic Retrieval-Augmented Generation
Our approach achieves several targets: (1) lifting the length limitations of the context window, saving on the prompt size; (2) allowing huge expansion of the number of retrieval entities available for the context; (3) alleviating the problem of misspelling or failing to find relevant entity names.
Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning
For such imaging techniques, the method can therefore significantly improve image quality, or maintain image quality with further reduced exposure time per image.
Optimizing the Procedure of CT Segmentation Labeling
In Computed Tomography, machine learning is often used for automated data processing.
A Knowledge Distillation framework for Multi-Organ Segmentation of Medaka Fish in Tomographic Image
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms.
Using the Order of Tomographic Slices as a Prior for Neural Networks Pre-Training
In this work, we propose a pre-training method SortingLoss.
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images
The problem is even more prominent for high-throughput tomography--an automated setup, capable of scanning large batches of samples without human interaction.
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations.
