Source code is publicly available on https://github.com/tiehangd/Para_DPMM/tree/master/Para_DPMM_package.

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Electroencephalogram (EEG) signal has large variance and its pattern differs significantly across subjects. Cross subject EEG classification is a challenging task due to such pattern variation and the limited target data available, as collecting and annotating EEG data for a new user is costly and involve efforts from human experts. We model the task as a transfer learning problem and propose to tackle it with meta learning on constrained transfer learning (MLCL). MLCL is an end to end trainable learning paradigm that trains on large standard datasets of known subjects and then quickly adapt to a new subject with minimal target data. The transfer process is accelerated by applying model-agnostic meta-learning (MAML) algorithm, performed under a novel constrained setting which keeps enough flexibility to adapt to new subject while significantly reducing number of parameters to transfer. This enables the adaptation done with a small amount of target data. The method can be applied to all deep learning oriented models. We performed extensive experiments across three public datasets. The proposed model outperforms current state of the arts in terms of both accuracy and AUC-ROC score for low target resource configurations. We further conducted interpretation analysis on the model, which reveals detailed information at the resolution of individual channels for the transfer process.

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Electroencephalogram(EEG) signal is widely used in brain computer interfaces (BCI), the pattern of which differs significantly across different subjects, and poses a major challenge for real world application of EEG classifiers. We found an efficient transfer learning method, named Meta UPdate Strategy (MUPS), boosts cross subject classification performance of EEG signals, and only need a small amount of data from target subject. The model tackles the problem with a two step process: (1) extract versatile features that are effective across all source subjects, and (2) adapt the model to target subject. The proposed model, which originates from meta learning, aims to find feature representation that is broadly suitable for different subjects, and maximizes sensitivity of the loss function on new subject such that one or a small number of gradient steps can lead to effective adaptation. The method can be applied to all deep learning oriented models. We performed extensive experiments on two public datasets, the proposed MUPS model outperforms current state of the arts by a large margin on accuracy and AUC-ROC when only a small amount of target data is used.Our code is publicly available at https://github.com/tiehangd/MUPS.

Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text and relationships among synonyms. In this paper, the documents are modeled as the joint of bags of words, sequential features and word embeddings. We proposed Sequential Embedding induced Dirichlet Process Mixture Model (SiDPMM) to effectively exploit this joint document representation in text clustering. The sequential features are extracted by the encoder-decoder component. Word embeddings produced by the continuous-bag-of-words (CBOW) model are introduced to handle synonyms. Experimental results demonstrate the benefits of our model in two major aspects: 1) improved performance across multiple diverse text datasets in terms of the normalized mutual information (NMI); 2) more accurate inference of ground truth cluster numbers with regularization effect on tiny outlier clusters.

In this paper, auto regression between neighboring observed variables is added to Dynamic Bayesian Network(DBN), forming the Auto Regressive Dynamic Bayesian Network(AR-DBN). The detailed mechanism of AR-DBN is specified and inference method is proposed. We take stock market index inference as example and demonstrate the strength of AR-DBN in latent variable inference tasks. Comprehensive experiments are performed on S&P 500 index. The results show the AR-DBN model is capable to infer the market index and aid the prediction of stock price fluctuation.

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