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Found 5 papers, 2 papers with code
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein
Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data.
ChemSafetyBench: Benchmarking LLM Safety on Chemistry Domain
To address this issue in the field of chemistry, we introduce ChemSafetyBench, a benchmark designed to evaluate the accuracy and safety of LLM responses.
BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching
The advanced capabilities of Large Language Models (LLMs) have inspired the development of various interactive web services or applications, such as ChatGPT, which offer query inference services for users.
GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data
Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation. Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels.
Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme
The modeling of optical wave propagation in optical fiber is a task of fast and accurate solving the nonlinear Schr\"odinger equation (NLSE), and can enable the optical system design, digital signal processing verification and fast waveform calculation.
