**Noise Contrastive Estimation (NCE)**

Noise contrastive estimation is essentially an ‘strategy’ to avoid a computationally expensive step in a language modeling task or word embedding generation task, when the the corpus is large.

A Gentle Introduction to Noise Contrastive Estimation … **Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie)**

In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie). Melanie is the first approach able to transfer knowledge between multiple data streaming sources in non-stationary environments. It creates several sub-classifiers to learn different aspects from different source and target concepts over time. The sub-classifiers that match the current target concept well are identified, and used to compose an ensemble for predicting examples from the target concept. We evaluate Melanie on several synthetic data streams containing different types of concept drift and on real world data streams. The results indicate that Melanie can deal with a variety drifts and improve predictive performance over existing data stream learning algorithms by making use of multiple sources. … **Audio-Visual Sequence-to-Sequence Dual Network (AVSDN)**

Audio-visual event localization requires one to identify the event which is both visible and audible in a video (eitherat a frame or video level). To address this task, we propose a deep neural network named Audio-Visual sequence-to-sequence dual network (AVSDN). By jointly taking bothaudio and visual features at each time segment as inputs, ourproposed model learns global and local event information ina sequence to sequence manner, which can be realized in either fully supervised or weakly supervised settings. Empirical results confirm that our proposed method performs favorably against recent deep learning approaches in both settings. … **Principal Component Regression (PCR)**

In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). Typically, it considers regressing the outcome (also known as the response or the dependent variable) on a set of covariates (also known as predictors, or explanatory variables, or independent variables) based on a standard linear regression model, but uses PCA for estimating the unknown regression coefficients in the model. In PCR, instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regressors. One typically uses only a subset of all the principal components for regression, thus making PCR some kind of a regularized procedure. Often the principal components with higher variances (the ones based on eigenvectors corresponding to the higher eigenvalues of the sample variance-covariance matrix of the explanatory variables) are selected as regressors. However, for the purpose of predicting the outcome, the principal components with low variances may also be important, in some cases even more important. One major use of PCR lies in overcoming the multicollinearity problem which arises when two or more of the explanatory variables are close to being collinear. PCR can aptly deal with such situations by excluding some of the low-variance principal components in the regression step. In addition, by usually regressing on only a subset of all the principal components, PCR can result in dimension reduction through substantially lowering the effective number of parameters characterizing the underlying model. This can be particularly useful in settings with high-dimensional covariates. Also, through appropriate selection of the principal components to be used for regression, PCR can lead to efficient prediction of the outcome based on the assumed model.

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