| Meetup No | Speaker | Title | Slide |
|---|---|---|---|
| #1 | Nguyễn Duy Khương | Deep Learning for Sparse Big Data in Recommendation Systems | |
| #1 | Đinh Quang Huy | Xây dựng AI startup | |
| #2 | Văn Phú Quang Huy, Tạ Đức Tùng, Khang Pham | Gradient Descent Optimization Algorithms | slide |
| #3 | Nguyễn Tuấn Dương, Nguyễn Phước Tất Đạt | Generative Adversarial Networks (GANs) | slide |
| #4 | Văn Phú Quang Huy | KaoNet v2: Face Translation using CycleGAN | slide |
| #5 | Văn Phú Quang Huy | 3 Challenges in Customer Feedback Classification | slide |
| #5 | Bùi Hồng Hà | From Hadoop to Data Ware House | slide |
| #6 | Khang Phạm | Word Embedding | slide |
| #7 | Đỗ Minh Hải | Recurent Neural Networks | slide |
| #8 | Cao Vũ Dũng | Sequence to Sequence Models: A Review | slide |
| #9 | Trần Công Khanh | HMM at a glance | slide |
| #9 | Nguyễn Phước Tất Đạt | An introduction to Mask-RCNN | slide |
| #10 | Phong Nguyễn | Deep Reinforcement Learning | slide |
| #10 | Nguyễn Duy Khương | Deep Reinforcement Learning for Recommendation Systems | |
| #11 | Nguyễn Tuấn Dương | Policy Gradients (PG) | slide |
| #11 | Phan Doãn Phúc | Triplet Loss – the Good the Bad and the Ugly | slide |
| #13 | Phạm Quang Khang | Pay More Attention to Attention Mechanism | slide |
| #13 | Nguyễn Phước Tất Đạt | Machine Can Do Reading Comprehension Test for You | [slide] [code] |
| #15 | Trần Quang Thiện | Deep learning in healthcare: Opportunities and challenges with electronic health records (EHR) data | slide |
| #16 | Lê Trung Kiên | Regularization in Deep Learning | slide |
| #18 | Tho Phan | From Seq2seq with Attention to Abstractive Text Summarization | slide |
| #18 | Phan Hoang Phuong | Introduction to Survival Analysis | slide |
| #19 | Nguyễn Tuấn Dương | Understand Transformer by going through the Annotated Transformer code | slide |
| #19 | Phạm Quang Khang | BERT as an application of Transformer and understanding the secret of BERT | slide |
| #20 | Nguyễn Anh Tuấn | BERT insights | [slide on BERT] [slide on distillation] [code] |
| #22 | Lê Tuấn Anh | DETR: End-to-end Object Detection with Transformers | [slide on DETR] |
| #23 | Nguyễn Sỹ Quân | Deeplab: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation | [slide on Deeplab] |
| No | Speaker | Title | Slide |
|---|---|---|---|
| 1 | Đỗ Minh Hải | [ICML] Learning Longer-term Dependencies in RNNs with Auxiliary Losses | slide |
| 2 | Phạm Quang Khang | [CVPR] MobileNetV2: Inverted Residuals and Linear Bottlenecks | slide |
| 3 | Trần Công Khanh | [ACL] Learning to Control the Specificity in Neural Response Generation | slide |
| 4 | Nguyễn Tuấn Dương | [ICML] Fixing a broken ELBO | slide |
| 5 | Nguyễn Phước Tất Đạt | [ACL] Simple and Effective Multi-Paragraph Reading Comprehension | slide |
| 6 | Văn Phú Quang Huy | [ACL] Hierarchical Neural Story Generation | slide |
| No | Speaker | Title | Slide |
|---|---|---|---|
| 1 | Anh Le | [ICLR 2019] Adversarial Reprogramming of Neural Networks | slide |
| 2 | Phan Hữu Thọ | [NeurIPS 2018] Precision and Recall for Time Series | slide |
| 3 | Nguyễn Tuấn Dương | [NeurIPS 2018] Neural Ordinary Differential Equations |
| No | Speaker | Title | Slide |
|---|---|---|---|
| 1 | Nguyễn Phước Tất Đạt | [KDD 2019] Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks | slide |
| 2 | Văn Phú Quang Huy | [CVPR 2019] Inverse Cooking: Recipe Generation from Food Images | slide |
| 3 | Phạm Quang Khang | [CVPR 2018 - CVPR/ICML 2019] Effort and achievement of AutoML in neural architecture search - From NasNet to Efficient Net and further | slide |
| 4 | Nguyễn Vũ Thanh Tùng | Overview of recent big language models |