1 code implementation • 5 Sep 2023 • Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Li, Bolin Ding, Jingren Zhou
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
1 code implementation • Conference on Neural Information Processing Systems 2022 • Zhenhong Sun, Ce Ge, Junyan Wang, Ming Lin, Hesen Chen, Hao Li, Xiuyu Sun
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage.
no code implementations • 29 Sep 2021 • Hesen Chen, Ming Lin, Xiuyu Sun, Rong Jin
In this work, we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss.
no code implementations • 12 Jul 2021 • Ya Wang, Hesen Chen, Fangyi Zhang, Yaohua Wang, Xiuyu Sun, Ming Lin, Hao Li
Data augmentation is a commonly used approach to improving the generalization of deep learning models.
2 code implementations • 1 Feb 2021 • Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin
Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.
Ranked #2 on Neural Architecture Search on ImageNet
2 code implementations • ICCV 2021 • Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin
To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures.
Neural Architecture Search Vocal Bursts Intensity Prediction
2 code implementations • 24 Jun 2020 • Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin
To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.