39 citations · 82 across the 9 of their papers we have counts for
9 papers
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Longhui Yu, Weisen Jiang, Han Shi +7
Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open…
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Weisen Jiang, Han Shi, Longhui Yu +4
Self-Consistency samples diverse reasoning chains with answers and chooses the final answer by majority voting. It is based on forward reasoning and cannot further improve performa…
ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis
Shoukang Hu, Kaichen Zhou, Kaiyu Li +6
Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse vi…
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap
Weiyang Liu, Longhui Yu, Adrian Weller +1
The neural collapse (NC) phenomenon describes an underlying geometric symmetry for deep neural networks, where both deeply learned features and classifiers converge to a simplex eq…
DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality
Yuqing Wang, Yizhi Wang, Longhui Yu +2
Vector font synthesis is a challenging and ongoing problem in the fields of Computer Vision and Computer Graphics. The recently-proposed DeepVecFont achieved state-of-the-art perfo…
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving
Longhui Yu, Yifan Zhang, Lanqing Hong +2
Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, ca…
Continual Learning by Modeling Intra-Class Variation
Longhui Yu, Tianyang Hu, Lanqing Hong +3
It has been observed that neural networks perform poorly when the data or tasks are presented sequentially. Unlike humans, neural networks suffer greatly from catastrophic forgetti…
Memory Replay with Data Compression for Continual Learning
Liyuan Wang, Xingxing Zhang, Kuo Yang +7
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves…
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification
Longhui Yu, Zhenyu Weng, Yuqing Wang +1
Incremental learning methods can learn new classes continually by distilling knowledge from the last model (as a teacher model) to the current model (as a student model) in the seq…