In recent years, multimodal learning has attracted an increasing interest. A special scenario of multimodal learning, learning on temporal data, is common but has not been well studied. In multimodal temporal data, not all modalities of a sample arrive at the same time. Because of that, different types of samples may have different importance in many use cases, where an early sample with significant modalities may be more valuable than a later one as early predictions can be made to speed up decision-making processes. Besides, sample correlations are very common in multimodal temporal data, as samples accumulate in time and a late sample may contain the same data existing in an earlier sample. Training without the awareness of the importance and correlation yields less effective models. In this work, we define multimodal temporal data, discuss key challenges and propose two methods that improve traditional multimodal training on such data. We demonstrate the effectiveness of the proposed methods on several multimodal temporal datasets, where they show 1% to 3% improvements over the baseline.

Automated feature engineering (AutoFE) is the process of automatically building and selecting new features that help improve downstream predictive performance. While traditional feature engineering requires significant domain expertise and time-consuming iterative testing, AutoFE strives to make feature engineering easy and accessible to all data science practitioners. We introduce a new AutoFE algorithm, IIFE, based on determining which feature pairs synergize well through an information-theoretic perspective called interaction information. We demonstrate the superior performance of IIFE over existing algorithms. We also show how interaction information can be used to improve existing AutoFE algorithms. Finally, we highlight several critical experimental setup issues in the existing AutoFE literature and their effects on performance.

While many methods such as Locally Interpretable Model-agnostic Explanation (LIME), Integrated Gradients and Layerwise Relevance Propagation (LRP) have been developed to explain how recurrent neural networks make predictions, the explanations generated by each method often times vary dramatically. There is no consensus about which explainability method most accurately and robustly determine features important for model prediction. We consider a classification task on a sequence of events with different types and apply both gradient-based and attention-based explanation models to compute explanations on the event type level. We show that attention-based models return a higher similarity score between explanations for models initialized with different random seeds. However, there are still significant differences in explanations between model runs. We develop an optimization-based model to find a low-loss, high-accuracy path between two sets of trained weights to understand how model explanations morph between different local minima. We use this low-loss path to provide insight as to why explanations vary on two sentiment datasets.

Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have known hierarchical relations, and classifiers exploiting these relations should perform better. We propose hierarchical classification models combining a CNN to extract hierarchical representations of images, and an RNN or sequence-to-sequence model to capture a hierarchical tree of classes. In addition, we apply residual learning to the RNN part in order to facilitate training our compound model and improve generalization of the model. Experimental results on a public and a real world proprietary dataset of images show that our hierarchical networks perform better than state-of-the-art CNNs.