COMP 5900 - Advanced Machine Learning

Topics and Papers for in-class Presentation

Below is the list of topics we are interested to discuss. Under each topic, some related papers are listed for you to consider. You are not limited to choose only from the below list. Pick a topic and search for recent papers in conferences such as NeurIPS, ICML, AAAI, EMNLP , ACL, CVPR or journals such as TPAMI, TCYB, TNNLS, JMLR. If you wish to choose other topics, contact me as soon as possible to see if it fits into the scope of the course. The deadline to indicate your topic of interest and submit your prefered papers is January 18, 2021.

Zero-shot Learning

• C. H. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification for zero-shot visual object categorizationa,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 3, pp. 453–465, 2014.
• E. Kodirov, T. Xiang, Z. Fu, and S. Gong, “Unsupervised Domain Adaptation for Zero-Shot Learning,” in ICCV, 2017.
• Z. Zhang and V. Saligrama, “Zero-shot learning via joint latent similarity embedding,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016.
• Y. Xian, Z. Akata, G. Sharma, Q. Nguyen, M. Hein, and B. Schiele, “Latent embeddings for zero-shot classification,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, 2016.
• E. Kodirov, T. Xiang, and S. Gong, “Semantic Autoencoder for Zero-Shot Learning”, in CVPR, 2017.
• Zhang, Fei, and Guangming Shi. "Co-Representation Network for Generalized Zero-Shot Learning." International Conference on Machine Learning, 2019.
• Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, “Transductive Multi-View Zero-Shot Learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 11, pp. 2332–2345, 2015.

Few-shot Learning

• Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. "Siamese neural networks for one-shot image recognition." ICML deep learning workshop. Vol. 2. 2015.
• Florian Schroff, Dmitry Kalenichenko, James Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering", CVPR 2015
• Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. "Matching Networks for One Shot Learning", NIPS 2016.
• J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in NIPS, 2017.
• Yoon, Sung Whan, Jun Seo, and Jaekyun Moon. "TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning." ICML (2019).
• Li, Huaiyu, et al. "LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning." ICML (2019).
• F. Sung, Y. Yang, and L. Zhang, “Learning to Compare : Relation Network for Few-Shot Learning” CVPR 2018.
• Ravi, S., & Larochelle, H. (2017). Optimization as a Model for Few-Shot Learning. In ICLR 2017

GANs

• Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.
• Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
• Karras, Tero, Samuli Laine, and Timo Aila. "A style-based generator architecture for generative adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2019.
• Choi, Yunjey, et al. "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

Deep Clustering, Multiview Clustering

• Xie, Junyuan, Ross Girshick, and Ali Farhadi. "Unsupervised deep embedding for clustering analysis." International conference on machine learning. 2016.
• Yang, Bo, et al. “Towards k-means-friendly spaces: Simultaneous deep learning and clustering.” Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.
• Lin, Wen-Yan, Siying Liu, Jian-Huang Lai, and Yasuyuki Matsushita. "Dimensionality's Blessing: Clustering Images by Underlying Distribution." CVPR, 2018.
• Shaham, Uri, et al. "Spectralnet: Spectral clustering using deep neural networks." ICLR, 2018
• KumarA,DaumH, "A co-training approach for multi-viewspectral clustering", Proceedings of the 28th international conference on machine learning. ACM, 2011
• Gao H, Nie F, Li X, Huang H, "Multi-view subspace clustering" IEEE international conference on computer vision, 2015.

Transfer Learning and Domain Adaptation

• B. Fernando, et.al., "Unsupervised Visual Domain Adaptation Using Subspace Alignment", ICCV 2013.
• Zamir, Sax, Shen, Guibas, Malik, Savarese, “Taskonomy: Disentangling Task Transfer Learning”, CVPR 2018.
• A. Asgarian and A. Sibilia, “A Hybrid Instance-based Transfer Learning Method,” 2018.
• J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in NIPS, 2014, pp. 1–9.
• S. Kornblith, J. Shlens, and Q. V. Le “Do Better ImageNet Models Transfer Better?”, 2018
• Z. Li and D. Hoiem, “Learning without Forgetting”, 2016.

Multi-view Learning

• Blum A,Mitchell T, "Combining labeled and unlabeled data with co-training" ACM, 1998.
• Sindhwani V, Niyogi P, BelkinM, "A co-regularization approach to semi-supervised learning with multiple views", ICML, 2005.
• Hardoon DR, Szedmak SR, Shawe-taylor JR, "Canonical correlation analysis: an overview with application to learning methods", Neural Comput, 2004.
• Kan, Meina, et al. "Multi-view discriminant analysis." IEEE transactions on pattern analysis and machine intelligence, 2015.
• D. Yi, Z. Lei, and S. Z. Li, “Shared representation learning for heterogenous face recognition,” 2015 11th IEEE Int. Conf. Work. Autom. Face Gesture Recognition, FG 2015, vol. 1, pp. 1–7, 2015.
• J. Hu, J. Lu, S. Member, and Y.-P. Tan, “Sharable and Individual Multi-View Metric Learning,” TPAMI, 2018.

Interpretability of ML

• Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, “Why Should I Trust You?: Explaining the Predictions of Any Classifier”, KDD 2016
• Scott Lundberg, Su-In Lee, “A Unified Approach to Interpreting Model Predictions” , NIPS 2017
• Marco Ribeiro, Sameer Singh, Carlos Guestrin, “Anchors: High-Precision Model-Agnostic Explanations”, AAAi 2018
• Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo, “Examples are not enough, learn to criticize! criticism for interpretability”, 2016.
• P. W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” best paper award ICML 2017
• A. Dhurandhar et al. “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives”, 2018
• Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2014), “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation”.
• Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory sayres , “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)”, ICML 2018.
• J. Adebayo et al., “Sanity Checks for Saliency Maps,” NIPS, 2018. Spot light
• I. Y. Chen, F. D. Johansson, and D. Sontag, “Why Is My Classifier Discriminatory?,” spot light, in NIPS, 2018.
• I. Lage, A. Slavin Ross, B. Kim Google Brain, S. J. Gershman, and F. Doshi-Velez, “Human-in-the-Loop Interpretability Prior,” spot light in NIPS, 2018.
• SA Friedler, C Scheidegger, (2016), “On the (im) possibility of fairness”.