image_embedding:meeting
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meeting schedule
2019/09/03
- S. Chopra, R. Hadsell and Y. LeCun, Learning a similarity metric discriminatively, with application to face verification,“ 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005, pp. 539-546 vol.
- Presenter: Shi Lu
Abstract: Early version of contrastive loss and Smilarity metric
2019/09/11
- Wu C Y , Manmatha R , Smola A J , et al. Sampling Matters in Deep Embedding Learning. 2017.
- Presenter: Yicong
Abstract: distance weighted sampling,different version of triplet loss
2019/09/17
- David Qiu, Embedding and latent variable models using maximal correlation, 2016
- Presenter: Jiarong
2019/09/28
- Monath, N., Zaheer, M., Silva, D., McCallum, A., & Ahmed, A. (2019, July). Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 714-722). ACM.
- Presenter: Runpeng
- Wang, L., Wu, J., Huang, S. L., Zheng, L., Xu, X., Zhang, L., & Huang, J. (2019, July). An efficient approach to informative feature extraction from multimodal data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 5281-5288).
- Presenter: Calvin
2019/10/13
- Kingma, Diederik P., and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
- Presenter: Shi Lu
2019/10/17
- Narayanaswamy, S., Paige, T. B., Van de Meent, J. W., Desmaison, A., Goodman, N., Kohli, P., … & Torr, P. (2017). Learning disentangled representations with semi-supervised deep generative models. In Advances in Neural Information Processing Systems (pp. 5925-5935).
- Presenter: Shi Lu
image_embedding/meeting.1571469579.txt.gz · Last modified: 2019/10/19 03:19 by yang