li_223.tex:161:236: Error: Solo `)' found. However, all these methods share the same shortcoming – they consider the number of clusters (\ie, $k$ in $k$-means) as a hyperparameter that needs to be specified by the user. This brings severe restrictions to their applications: 1) most of these algorithms are sensitive to the choice of $k$; 2) users lack the prior knowledge of the number of clusters; 3) this value itself may be time-varying (\eg, $k$ may increase as more data is accumulated); 4) in some scenarios, particularly for large datasets, there is no golden ground truth for this value \citep{li2018learning}.  
li_223.tex:161:300: Error: Solo `)' found. However, all these methods share the same shortcoming – they consider the number of clusters (\ie, $k$ in $k$-means) as a hyperparameter that needs to be specified by the user. This brings severe restrictions to their applications: 1) most of these algorithms are sensitive to the choice of $k$; 2) users lack the prior knowledge of the number of clusters; 3) this value itself may be time-varying (\eg, $k$ may increase as more data is accumulated); 4) in some scenarios, particularly for large datasets, there is no golden ground truth for this value \citep{li2018learning}.  
li_223.tex:161:361: Error: Solo `)' found. However, all these methods share the same shortcoming – they consider the number of clusters (\ie, $k$ in $k$-means) as a hyperparameter that needs to be specified by the user. This brings severe restrictions to their applications: 1) most of these algorithms are sensitive to the choice of $k$; 2) users lack the prior knowledge of the number of clusters; 3) this value itself may be time-varying (\eg, $k$ may increase as more data is accumulated); 4) in some scenarios, particularly for large datasets, there is no golden ground truth for this value \citep{li2018learning}.  
li_223.tex:161:455: Error: Solo `)' found. However, all these methods share the same shortcoming – they consider the number of clusters (\ie, $k$ in $k$-means) as a hyperparameter that needs to be specified by the user. This brings severe restrictions to their applications: 1) most of these algorithms are sensitive to the choice of $k$; 2) users lack the prior knowledge of the number of clusters; 3) this value itself may be time-varying (\eg, $k$ may increase as more data is accumulated); 4) in some scenarios, particularly for large datasets, there is no golden ground truth for this value \citep{li2018learning}.  
li_223.tex:203:161: Error: Solo `)' found. Here $\mPhi$ is the set of hyperparameters that specify the prior, which will soon be introduced in the next section. We emphasize the challenges of the task: 1) the number of clusters $K$ is unknown; 2) $\mtheta$ parameterizes a deep neural network which needs to be learned; 3) the cluster information (\ie, $\mmu_k$ and $\lambda_k$) need to be computed at the same time. In what follows we will see how the proposed method can address all these challenges under a unified framework.  
li_223.tex:203:203: Error: Solo `)' found. Here $\mPhi$ is the set of hyperparameters that specify the prior, which will soon be introduced in the next section. We emphasize the challenges of the task: 1) the number of clusters $K$ is unknown; 2) $\mtheta$ parameterizes a deep neural network which needs to be learned; 3) the cluster information (\ie, $\mmu_k$ and $\lambda_k$) need to be computed at the same time. In what follows we will see how the proposed method can address all these challenges under a unified framework.  
li_223.tex:203:279: Error: Solo `)' found. Here $\mPhi$ is the set of hyperparameters that specify the prior, which will soon be introduced in the next section. We emphasize the challenges of the task: 1) the number of clusters $K$ is unknown; 2) $\mtheta$ parameterizes a deep neural network which needs to be learned; 3) the cluster information (\ie, $\mmu_k$ and $\lambda_k$) need to be computed at the same time. In what follows we will see how the proposed method can address all these challenges under a unified framework.  
