Zhe Gan

Zhe Gan is a person.

• See: KOSMOS-1 Architecture, Automated Text Generation, Visual Instruction Tuning.

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References

• https://scholar.google.com/citations?user=E64XWyMAAAAJ&hl=en

2024

• (McKinzie et al., 2024) ⇒ Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah et al. (2024). “MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.” arXiv preprint arXiv:2403.09611

2021

• (Lei et al., 2021) ⇒ J Lei, L Li, L Zhou, Zhe Gan, TL Berg, M Bansal, and J Liu. (2021). “Less is More: Clipbert for Video-and-Language Learning via Sparse Sampling.” In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • NOTE: It proposes a novel approach for efficient video-and-language learning, utilizing sparse sampling to reduce computational requirements while maintaining high performance levels.

2020

• (Chen et al., 2020) ⇒ YC Chen, L Li, L Yu, A El Kholy, F Ahmed, Zhe Gan, Y Cheng, and J Liu. (2020). “Uniter: Universal Image-Text Representation Learning.” In: European Conference on Computer Vision, Pages 104-120.
  • NOTE: It introduces a method for learning universal image-text representations, aimed at improving the interoperability between visual and textual data in various computer vision tasks.

2019

• (Sun et al., 2019) ⇒ S Sun, Y Cheng, Zhe Gan, and J Liu. (2019). “Patient Knowledge Distillation for BERT Model Compression.” In: arXiv preprint arXiv:1908.09355.
  • NOTE: It introduces a method for compressing BERT models through knowledge distillation, focusing on preserving model performance while reducing model size.

2018

• (Xu et al., 2018) ⇒ T Xu, P Zhang, Q Huang, H Zhang, Zhe Gan, X Huang, and X He. (2018). “Attngan: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • NOTE: It presents an attentional generative adversarial network for generating detailed images from textual descriptions, focusing on the fine-grained aspects of the generated images.

2017

• (Zhang, Gan et al., 2017) ⇒ Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin. (2017). “Adversarial Feature Matching for Text Generation". In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017).

2016

• (Pu et al., 2016) ⇒ Y Pu, Zhe Gan, R Henao, X Yuan, C Li, A Stevens, and L Carin. (2016). “Variational Autoencoder for Deep Learning of Images, Labels, and Captions.” In: NIPS.
  • NOTE: It discusses the application of variational autoencoders in learning joint representations of images, labels, and captions, contributing to the field of multi-modal learning.

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