150 citations · 150 across the 5 of their papers we have counts for
5 papers
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Baolin Peng, Michel Galley, Pengcheng He +8
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. Howev…
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
Jilin Mei, Junbao Zhou, Yu Hu
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible wa…
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Shun Lu, Yu Hu, Longxing Yang +4
Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. Howe…
Uniform tensor clustering by jointly exploring sample affinities of various orders
Hongmin Cai, Fei Qi, Junyu Li +4
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, result…
Source-Free Domain Adaptation for Real-world Image Dehazing
Hu Yu, Jie Huang, Yajing Liu +3
Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images…