Paper Reviews 2019 Q1
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작성일
2020-03-12 11:01
조회
987
Seminar 기간: 2018. 12. 1 ~ 2019. 4. 1
참여 인원:
참여 인원:
- 지도교수 강필성
- 박사과정 김준홍, 김창엽
- 통합과정 김형석, 김동화, 박민식, 서승완, 박경찬, 이정호
- 석사과정 정재윤, 장명준, 송서하, 손규빈, 정민성, 최희정, 양우식, 천우진, 박중민, 이정훈, 조규원, 노영빈, 최종현
| 일시 | 주제 | 발표자 | 자료 |
| 2018-12-28 | Lifelong Learning With Dynamically Expandable Networks | 최희정 | 발표자료
발표영상 |
| 2018-12-28 | Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | 서승완 | 발표자료
발표영상 |
| 2019-01-04 | Semi-Supervised Learning with Ladder Networks | 양우식 | 발표자료
발표영상 |
| 2019-01-04 | Violent Flows: Real-Time Detection of Violent Crowd Behavior Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help? | 김동화 | 발표자료
발표영상 |
| 2019-01-08 | Learning how to explain neural networks: PatternNet and PatternAttribution | 손규빈 | 발표자료
발표영상 |
| 2019-01-08 | SQuAD: 100,000+ Questions for Machine Comprehension of Text Know What You Don’t Know: Unanswerable Questions for SQuAD | 김형석 | 발표자료
발표영상 |
| 2019-01-11 | Rich feature hierarchies for accurate object detection and semantic segmentation OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | 천우진 | 발표자료
발표영상1 발표영상2 발표영상3 |
| 2019-01-11 | Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series | 김창엽 | 발표자료
발표영상 |
| 2019-01-28 | Video-to-Video Synthesis | 송서하 | 발표자료
발표영상 |
| 2019-01-28 | World Models | 박민식 | 발표자료
발표영상 |
| 2019-01-31 | Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures Learning to Detect Violent Videos using Convolutional Long Short-Term Memory | 이정호 | 발표자료
발표영상 |
| 2019-01-31 | Spherical CNNs | 박경찬 | 발표자료
발표영상 |
| 2019-02-11 | Neural machine translation by jointly learning to align and translate Effective approaches to attention-based neural machine translation Feed-forward networks with attention can solve some long-term memory problems | 정민성 | 발표자료
발표영상 |
| 2019-02-11 | An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling | 김준홍 | 발표자료
발표영상 |
| 2019-02-15 | Visualizing and understanding convolutional networks Explaining nonlinear classification decisions with deep taylor decomposition | 최희정 | 발표자료
발표영상 |
| 2019-02-15 | Real-world anomaly detection in surveillance videos Violence detection in surveillance video using low-level features | 서승완 | 발표자료
발표영상 |
| 2019-02-25 | Attention is all you need BERT: Pretraining of deep bidirectional transformers for language understanding | 김동화 | 발표자료
발표영상 |
| 2019-03-07 | DiSAN: Directional self-attention network for RNN_CNN-free language understanding | 정민성 | 발표자료
발표영상 |
| 2019-03-14 | Fast R-CNN & Faster R-CNN | 양우식 | 발표자료
발표영상 |
| 2019-03-21 | 3D human pose estimation in video with temporal convolutions and semi-supervised training Eye in the sky | 손규빈 | 발표자료
발표영상 |
| 2019-03-28 | R-Net_Machine reading comprehension with self-matching networks | 김형석 | 발표자료
발표영상 |
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Data Science & Business Analytics Lab.
School of Industrial Management Engineering
College of Engineering, Korea University
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