Search Results for author:
Found 5 papers, 4 papers with code
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation
To address these concerns, we propose a novel Cross-head mutual mean-teaching Network (CMMT-Net) incorporated strong-weak data augmentation, thereby benefitting both self-training and consistency learning.
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
TIVE: A Toolbox for Identifying Video Instance Segmentation Errors
More importantly, as the fundamental model abilities demanded by the task, spatial segmentation and temporal association are still understudied in both evaluation and interaction mechanisms.
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.
