Loss-Based AdaBoost (LoAdaBoost)
Medical data are valuable for improvement of health care, policy making and many other purposes. Vast amount of medical data are stored in different locations ,on many different devices and in different data silos. Sharing medical data among different sources is a big challenge due to regulatory , operational and security reasons. One potential solution is federated machine learning ,which a method that sends machine learning algorithms simultaneously to all data sources ,train models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. In this article, we proposed an adaptive boosting method that increases the efficiency of federated machine learning. Using intensive care unit data from hospital, we showed that LoAdaBoost federated learning outperformed baseline method and increased communication efficiency at negligible additional cost. …

Graph-Based Broad Behavior-Aware Network (G-BBAN)
In this paper, we propose a heuristic recommendation system for interactive news, called the graph-based broad behavior-aware network (G-BBAN). Different from most of existing work, our network considers six behaviors that may potentially be conducted by users, including unclick, click, like, follow, comment, and share. Further, we introduce the core and coritivity concept from graph theory into the system to measure the concentration degree of interests of each user, which we show can help to improve the performance even further if it’s considered. There are three critical steps in our recommendation system. First, we build a structured user-dependent interaction behavior graph for multi-level and multi-category data as a preprocessing step. This graph constructs the data sources and knowledge information which will be used in G-BBAN through representation learning. Second, for each user node on the graph, we calculate its core and coritivity and then add the pair as a new feature associated to this user. According to the definition of core and coritivity, this user-dependent feature provides useful insights into the concentration degree of his/her interests and affects the trade-off between accuracy and diversity of the personalized recommendation. Last, we represent item (news) information by entity semantics and environment semantics; design a multi-channel convolutional neural network called G-CNN to learn the semantic information and an attention-based LSTM to learn user’s behavior representation; combine with previous concentration feature and input into another two fully connected layers to finish the classification task. The whole network consists of the final G-BBAN. Through comparing with baselines and several variates of itself, our proposed method shows the superior performance in extensive experiments. …

Kullback-Leibler – Local Interpretable Model-agnostic Explanations (KL-LIME)
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas from Bayesian projection predictive variable selection methods. The information theoretic basis helps in navigating the trade-off between explanation fidelity and complexity. We demonstrate the method in explaining MNIST digit classifications made by a Bayesian deep convolutional neural network. …

Action2Vec
We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train our embedding using a joint loss that combines classification accuracy with similarity to Word2Vec semantics. We evaluate Action2Vec by performing zero shot action recognition and obtain state of the art results on three standard datasets. In addition, we present two novel analogy tests which quantify the extent to which our joint embedding captures distributional semantics. This is the first joint embedding space to combine verbs and action videos, and the first to be thoroughly evaluated with respect to its distributional semantics. …