A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
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1 | Date | Presenter | Topic | Paper Title | Conferences | Paper URL | Slides | Online Meeting URL | ||||||||||||||||||
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3 | Jan 6 | Jiawei Zhang | Introduction | Course Introduction. | NA | NA | https://drive.google.com/file/d/1WeTYYWm_UD7tZL6Z93LdLuo30g19f79m/view?usp=sharing | |||||||||||||||||||
4 | Jan 8 | Jiawei Zhang | Introduction | Machine Learning Overview (Sections 2.1-2.4) | Book Chapter | http://www.ifmlab.org/files/book/broad_learning/chap2.pdf | https://drive.google.com/file/d/1UdStOxdRfu0UlrbtfhSW30KnP1bNAjbH/view?usp=sharing | |||||||||||||||||||
5 | Jan 13 | Jiawei Zhang | Introduction | Machine Learning Overview (Sections 2.5-2.7) | Book Chapter | http://www.ifmlab.org/files/book/broad_learning/chap2.pdf | https://drive.google.com/file/d/1GAIa6OOXfFlXQxc2OAnaG26u1VkZwfeD/view?usp=sharing | |||||||||||||||||||
6 | A Hands-on Tutorial Introduction | NA | NA | https://drive.google.com/file/d/1DYTEFPsmKRQ_bQMFB6SGS1vYZbQxg6_1/view?usp=sharing | ||||||||||||||||||||||
7 | Jan 15 | Haeseung Jeong | Deep Learning & Optimization Algorithms | An overview of gradient descent optimization algorithms | arXiv | https://arxiv.org/pdf/1609.04747.pdf | https://drive.google.com/open?id=1zpGLVgHKHx8aXpmRect6YKJywNU1-2lw | |||||||||||||||||||
8 | Jan 22 | Yixin Chen | Deep Learning & Optimization Algorithms | On the Variance of the Adaptive Learning Rate and Beyond | ICLR'20 | https://arxiv.org/abs/1908.03265 | https://drive.google.com/open?id=1xgky5VVCq4N4YrmvCdsFm3RRZacwtL1j | |||||||||||||||||||
9 | Jan 27 | Mohsen Gavahi | Deep Learning & Optimization Algorithms | Putting An End to End-to-End: Gradient-Isolated Learning of Representations | NIPS'19 | https://papers.nips.cc/paper/8568-putting-an-end-to-end-to-end-gradient-isolated-learning-of-representations.pdf | https://drive.google.com/open?id=11JF5Ekg2F5cbKUMX4_XgpqNJ7Tw0C2eZ | |||||||||||||||||||
10 | Jan 29 | Daniel Bis | Text Mining & NLP | XLNet: Generalized Autoregressive Pretraining for Language Understanding | NIPS'19 | https://papers.nips.cc/paper/8812-xlnet-generalized-autoregressive-pretraining-for-language-understanding.pdf | https://drive.google.com/open?id=12Cx5LhSDuV8LyBTOcF_1JMwh-2UiodrD | |||||||||||||||||||
11 | Feb 3 | Yan Ai | Deep Learning & Optimization Algorithms | Weight Agnostic Neural Networks | NIPS'19 | https://papers.nips.cc/paper/8777-weight-agnostic-neural-networks | https://drive.google.com/open?id=1trRbAiSLNYG1CpUy4s7l9GNOD8NwMXX7 | |||||||||||||||||||
12 | Feb 5 | Yixin Chen | Deep Learning & Optimization Algorithms | Probabilistic Logic Neural Networks for Reasoning | NIPS'19 | https://papers.nips.cc/paper/8987-probabilistic-logic-neural-networks-for-reasoning.pdf | https://drive.google.com/open?id=17rIzHpt11BkoROSSCOUw5vVkwCKOL9aZ | |||||||||||||||||||
13 | Feb 10 | Haopeng Zhang | Text Mining & NLP | Code completion with neural attention and pointer networks | IJCAI'18 | https://www.ijcai.org/proceedings/2018/0578.pdf | https://drive.google.com/open?id=12eCharGGdhDUAoA-l8FsNhWCweoEP0lI | |||||||||||||||||||
14 | Feb 12 | Jiyang Bai | Network Embedding & Graph Mining | Hyperbolic Graph Neural Networks | NIPS'19 | https://papers.nips.cc/paper/9033-hyperbolic-graph-neural-networks.pdf | https://drive.google.com/open?id=1-Z9sKqRuYV6ushdCInAQWZlHhWcSfp-s | |||||||||||||||||||
15 | Feb 17 | Jiyang Bai | Network Embedding & Graph Mining | Hyperbolic Graph Convolutional Neural Networks | NIPS'19 | https://papers.nips.cc/paper/8733-hyperbolic-graph-convolutional-neural-networks.pdf | https://drive.google.com/open?id=10g6O-xTxsYNsj6JrKh707heLRn-zWaBM | |||||||||||||||||||
16 | Feb 19 | Yixin Chen | Network Embedding & Graph Mining | Graph Classification using Structural Attention | KDD'18 | http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf | https://drive.google.com/open?id=1AKVBL-N6HdSC-nujdVmqlXYl-t_N9rGi | |||||||||||||||||||
17 | Feb 24 | Yuxiang Ren | Network Embedding & Graph Mining | Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels | NIPS'19 | https://papers.nips.cc/paper/8809-graph-neural-tangent-kernel-fusing-graph-neural-networks-with-graph-kernels.pdf | https://drive.google.com/open?id=17e-AiIUd4WabieY0jqF69uqmDB_wQ69K | |||||||||||||||||||
18 | Feb 26 | Yuxiang Ren | Network Embedding & Graph Mining | Universal Invariant and Equivariant Graph Neural Networks | NIPS'19 | https://papers.nips.cc/paper/8931-universal-invariant-and-equivariant-graph-neural-networks.pdf | https://drive.google.com/open?id=1N9arFSTZxcZo1_yt7woAc5WWshApSdnk | |||||||||||||||||||
19 | March 2 | Lin Meng | Network Embedding & Graph Mining | Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks | NIPS'19 | https://papers.nips.cc/paper/9303-layer-dependent-importance-sampling-for-training-deep-and-large-graph-convolutional-networks.pdf | https://drive.google.com/open?id=1k8G6mLSLl1fhzFMJAL8z8er6nHuQ8jL_ | |||||||||||||||||||
20 | March 4 | Mohsen Gavahi | Deep Learning & Optimization Algorithms | Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent | NIPS'19 | https://papers.nips.cc/paper/9063-wide-neural-networks-of-any-depth-evolve-as-linear-models-under-gradient-descent.pdf | https://drive.google.com/open?id=1C5o0QmEZ5AUL4CgHZiEmMSHYm1QeCy2r | |||||||||||||||||||
21 | March 9 | Haopeng Zhang | Text Mining & NLP | Bridging the Gap between Training and Inference for Neural Machine Translation | ACL'19 | https://www.aclweb.org/anthology/P19-1426.pdf | https://drive.google.com/open?id=1NOOI309OF9SQoStqMvDyXhPFrqCMAR9F | |||||||||||||||||||
22 | March 11 | Lin Meng | Deep Learning & Optimization Algorithms | Towards Understanding the Importance of Shortcut Connections in Residual Networks | NIPS'19 | https://papers.nips.cc/paper/9003-towards-understanding-the-importance-of-shortcut-connections-in-residual-networks.pdf | https://drive.google.com/open?id=14Z2kVnReC6r0r4Lr3ISi60PI96ndbGj_ | Paste your meeting room invitation URL below | ||||||||||||||||||
23 | March 23 | Xibo Chen | Text Mining & NLP | LambdaNet: Probabilistic Type Inference using Graph Neural Networks | ICLR'20 | https://openreview.net/forum?id=Hkx6hANtwH | https://drive.google.com/open?id=1O47VfswIk4QkmjMY9EBhjXaDQLU_ML0a | https://zoom.us/j/6524445035 | ||||||||||||||||||
24 | March 25 | Daniel Bis | Text Mining & NLP | Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks | ICLR'19 | https://arxiv.org/pdf/1810.09536.pdf | https://drive.google.com/open?id=165GA_cORe_WKZbNfny2OA_LLHqAT_kPS | https://fsu.zoom.us/j/620689811 | ||||||||||||||||||
25 | March 30 | Yan Ai | Image & Vision | Exploring Visual Relationship for Image Captioning | CVPR'18 | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ting_Yao_Exploring_Visual_Relationship_ECCV_2018_paper.pdf | https://drive.google.com/open?id=1_35rjOa5hUvisWRy5krAbCaTZEjwHmO4 | https://us04web.zoom.us/j/3635363945 | ||||||||||||||||||
26 | April 1 | Jiyang Bai | Image & Vision | Graph R-CNN for Scene Graph Generation | CVPR'18 | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jianwei_Yang_Graph_R-CNN_for_ECCV_2018_paper.pdf | https://drive.google.com/open?id=1seSAP0ohaRqYbd9O2NOtajFiKdmzz_Rb | https://us04web.zoom.us/j/963423347?pwd=VENnd284ZGJQUjVBZEVVRXRJK3V4QT09 | ||||||||||||||||||
27 | April 6 | Xibo Chen | Image & Vision | Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | CVPR'19 | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf | https://drive.google.com/open?id=1g5J6D5Yi1Nxe3u8-IjNtprXcp6b8xJm1 | https://zoom.us/j/6524445035 | ||||||||||||||||||
28 | April 8 | Haeseung Jeong | Image & Vision | A Style-Based Generator Architecture for Generative Adversarial Networks | CVPR'19 | https://arxiv.org/abs/1812.04948 | https://drive.google.com/open?id=1b2qxLC9XaQWTsGsnrVTkUhXvRKU2m37J | https://us04web.zoom.us/j/2619210229 | ||||||||||||||||||
29 | April 13 | Haopeng Zhang | Image & Vision | ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness | ICLR'19 | https://openreview.net/forum?id=Bygh9j09KX | https://drive.google.com/open?id=1yC5kY0dfwCxNVqLu0pNfVmN-aF7FIXr3 | https://fsu.zoom.us/j/701929552 | ||||||||||||||||||
30 | April 15 | Yili Ren | Recommendation | Hierarchical Gating Networks for Sequential Recommendation | KDD'19 | https://www.kdd.org/kdd2019/accepted-papers/view/hierarchical-gating-networks-for-sequential-recommendation | https://drive.google.com/open?id=13jrMn_TpfVfpNHaFdpd1fGnmy4FHH1Wg | https://us04web.zoom.us/j/76529829951?pwd=cnkzVDBLMnRSNENQMUZwNWR3S1NTQT09 | ||||||||||||||||||
31 | April 20 | Yili Ren | Recommendation | Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination | KDD'19 | https://www.kdd.org/kdd2019/accepted-papers/view/dual-sequential-prediction-models-linking-sequential-recommendation-and-inf | https://drive.google.com/open?id=1lwYb8hKDESyYy8__WDbJsIAa3xCFeIJ5 | https://us04web.zoom.us/j/75705455430?pwd=Q0JHVjFPUVZzM1V4OEhEWTVJL21Ndz09 | ||||||||||||||||||
32 | April 22 | Lin Meng | Recommendation | KGAT: Knowledge Graph Attention Network for Recommendation | KDD'19 | https://www.kdd.org/kdd2019/accepted-papers/view/kgat-knowledge-graph-attention-network-for-recommendation | https://fsu.zoom.us/j/96147878709 | |||||||||||||||||||
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