3 accepted papers
Learning from irregularly sampled, streaming, multi-variate time-series data with many missing values is a very challenging task. In this paper, we propose a Bayesian Continual Imputation and Prediction for Time-series Data (B-CIPIT), for learning from a sequence of time-series tasks. First, we deve…
Knowledge Tracing is the prediction of the future performance of a learner, given the past performance. The existing knowledge tracing models represent the training data and does not generalize when there is a drift in the data distribution. We first empirically demonstrate an evolving Knowledge Tra…
We propose an online continual learning algorithm based on an enhanced Random Vector Functional Link Network (OCL-eRVFL), that learns a sequence of tasks continually, where each task is defined by streaming data with each sample arriving once and only once. As data for a new task in domain increment…