Difference between revisions of "CSE 7300 Research Seminar"

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Rumi, Masuma Akter, et al. "Accelerating sparse cnn inference on gpus with performance-aware weight pruning." Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques. 2020. [https://dl.acm.org/doi/abs/10.1145/3410463.3414648]
 
Rumi, Masuma Akter, et al. "Accelerating sparse cnn inference on gpus with performance-aware weight pruning." Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques. 2020. [https://dl.acm.org/doi/abs/10.1145/3410463.3414648]
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Bing
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Charles
  
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Hao, B., Zhu, H., & Paschalidis, I. C. (2020, December). Enhancing clinical bert embedding using a biomedical knowledge base. In 28th International Conference on Computational Linguistics 10.18653/v1/2020.coling-main.57
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Logé, C., Ross, E., Dadey, D. Y. A., Jain, S., Saporta, A., Ng, A. Y., & Rajpurkar, P. (2021). Q-Pain: a question answering dataset to measure social bias in pain management. Neural Information Processing Systems 2021 https://doi.org/10.13026/2tdv-hj07
  
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Poulain, Raphael, Mehak Gupta, and Rahmatollah Beheshti. "Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records." (2022). [https://proceedings.mlr.press/v182/poulain22a.html]
 
  
 
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=== ---Apr 19 ---===
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Charles
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Bing
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Poulain, Raphael, Mehak Gupta, and Rahmatollah Beheshti. "Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records." (2022). [https://proceedings.mlr.press/v182/poulain22a.html]
  
 
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Revision as of 04:45, 13 April 2023

  • Theme: AI and IoT for Medicine (AIM)
  • Instructor: Prof. Chenyang Lu
  • Semester: Spring 2023
  • Time: Wednesday at 11am-12pm
  • Location: McKelvey 1030
  • Guidelines:
  • Recommended sources: NeurIPS ([1]), ICML ([2]), AAAI ([3]), SIGKDD ([4]), IJCAI ([5]), IMWUT ([6]), HEALTH ([7]), Digital Medicine ([8]), Lancet Digital Health ([9]), SenSys, MobiSys, MobiCom

Presentation Schedule

---Jan 25 ---

Jaehwan

Jeong, Joo Seong, et al. "Band: coordinated multi-DNN inference on heterogeneous mobile processors." Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services. 2022.[10]

---Feb 01 ---

Hanyang

Contrastive multiview learning and its applications

Tian, Yonglong, Dilip Krishnan, and Phillip Isola. "Contrastive multiview coding." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. Springer International Publishing, 2020. [11]

Other related papers:

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition

Multi-level Feature Learning for Contrastive Multi-view Clustering

---Feb 08 ---

Ziqi

Mingcheng Chen. et al. Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21). Association for Computing Machinery, New York, NY, USA, 2663–2673.[12]

---Feb 15 ---

Ruiqi

Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer; MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 13587-13597 [13]

---Feb 22 ---

Melanie

F. Cheng et al., "VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 378-388, Jan. 2022, doi: 10.1109/TVCG.2021.3114836. [14]

---Mar 01 ---

Jingwen

Malinin, Andrey, Liudmila Prokhorenkova, and Aleksei Ustimenko. “Uncertainty in Gradient Boosting via Ensembles.” International Conference on Learning Representations, 2021. [15]

---Mar 08 ---

Andrew

Martinez, Natalia, Martin Bertran, and Guillermo Sapiro. "Minimax pareto fairness: A multi objective perspective." International Conference on Machine Learning. PMLR, 2020. [16].

---Mar 15 ---

Spring Break

---Mar 22 ---

Bye Week

---Mar 29 ---

Hangyue

---Apr 05 ---

Ye

Rumi, Masuma Akter, et al. "Accelerating sparse cnn inference on gpus with performance-aware weight pruning." Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques. 2020. [17]


---Apr 12 ---

Charles

Hao, B., Zhu, H., & Paschalidis, I. C. (2020, December). Enhancing clinical bert embedding using a biomedical knowledge base. In 28th International Conference on Computational Linguistics 10.18653/v1/2020.coling-main.57 Logé, C., Ross, E., Dadey, D. Y. A., Jain, S., Saporta, A., Ng, A. Y., & Rajpurkar, P. (2021). Q-Pain: a question answering dataset to measure social bias in pain management. Neural Information Processing Systems 2021 https://doi.org/10.13026/2tdv-hj07


---Apr 19 ---

Bing

Poulain, Raphael, Mehak Gupta, and Rahmatollah Beheshti. "Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records." (2022). [18]

---Apr 26 ---

Previous Semesters

Previous Lab Meetings

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