Papers by Yao-Hung Hubert Tsai
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization (P19-1)
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Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods to regularize multimodal data are imperfect due to imperfect modalities, missing entries or noise corruption. |
| Approach: | They propose a method to regularize multimodal data by tensor rank minimization . they use correlations between time and modalities to generate low-rank tenses . |
| Outcome: | The proposed model achieves good results across various levels of imperfection. |
Multimodal Transformer for Unaligned Multimodal Language Sequences (P19-1)
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Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
| Challenge: | Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. |
| Approach: | They propose a multimodal model that extends the standard Transformer network to learn representations directly from unaligned multimodal streams. |
| Outcome: | The proposed model outperforms state-of-the-art methods on aligned and non-aligned data. |
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (D19-1)
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| Challenge: | Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. |
| Approach: | They propose a new formulation of attention via the lens of the kernel which allows us to understand individual components of Transformer's attention. |
| Outcome: | The proposed model outperforms existing models on language understanding and sequence prediction tasks and is more efficient than existing models. |
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis (2020.emnlp-main)
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| Challenge: | Recent multimodal learning models with strong performances on human-centric tasks are often black-box with very limited interpretability. |
| Approach: | They propose a multimodal routing algorithm which dynamically adjusts weights between input and output modalities for each input sample. |
| Outcome: | The proposed model can interpret modality-prediction relationships globally and locally for each input sample while keeping competitive performance compared to state-of-the-art methods. |
Strong and Simple Baselines for Multimodal Utterance Embeddings (N19-1)
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| Challenge: | Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. |
| Approach: | They propose two simple but strong baselines to learn embeddings of multimodal utterances by factorizing the utterant into unimodal factors. |
| Outcome: | The proposed models show that they can be derived in closed form while maintaining simplicity and efficiency during learning and inference. |