Papers by Thanh-Nam Doan
Benchmarking Neural Topic Models: An Empirical Study (2021.findings-acl)
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| Challenge: | Neural topic modeling has been attracting much attention recently due to its ability to leverage the advantages of both neural networks and probabilistic topic models. |
| Approach: | They propose to evaluate neural topic models in three tasks using large datasets and a set of metrics to compare them. |
| Outcome: | The proposed models perform better in the first and third tasks than the traditional probabilistic models and are better in many cases. |
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts (2023.emnlp-main)
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Truong Do, Le Khiem, Quang Pham, TrungTin Nguyen, Thanh-Nam Doan, Binh Nguyen, Chenghao Liu, Savitha Ramasamy, Xiaoli Li, Steven Hoi
| Challenge: | Recent studies suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. |
| Approach: | They propose a method that dynamically generates router parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy. |
| Outcome: | Experiments on a wide range of tasks show that the proposed method performs better than existing methods. |