Summary of On the Identification Of Temporally Causal Representation with Instantaneous Dependence, by Zijian Li et al.
On the Identification of Temporally Causal Representation with Instantaneous Dependence
by Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Zhengmao Zhu, Guangyi Chen, Kun Zhang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Temporally causal representation learning aims to identify the latent causal process from time series observations without assuming instantaneous relations between variables. Existing methods require interventions or grouping of observations, which can be challenging to obtain in real-world scenarios. To address this gap, an IDentification framework for instantaneOus Latent dynamics (IDOL) is proposed by imposing a sparse influence constraint on the latent causal processes. This allows for identifiability results based on sufficient variability and sparse time-delayed and instantaneous relations. A temporally variational inference architecture is used to estimate the latent variables, while gradient-based sparsity regularization helps identify the latent causal process. Experimental results on simulation datasets demonstrate the method’s effectiveness in identifying the latent causal process. Furthermore, evaluations on human motion forecasting benchmarks with instantaneous dependencies illustrate the method’s real-world applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research focuses on understanding how things change over time and how they affect each other. Current methods require making assumptions that aren’t always true or need data that might be hard to get in real-life situations. To fix this, a new way is proposed to identify the underlying causes of changes in time series data by assuming that these causes only interact with each other through delays and not instantly. This approach uses machine learning techniques to find the underlying causes and ensure they are realistic. The results show that this method works well on simulated data and can also be applied to real-world problems, such as predicting human movements. |
Keywords
» Artificial intelligence » Inference » Machine learning » Regularization » Representation learning » Time series
