Finance is the efficient allocation of capital to achieve individual and societal objectives. Finance runs on information, from the number of ships under construction in the ports of Dalian, to the beliefs of investors in a marketplace--we want to put ...

Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In ...

The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time ...

Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and ...

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical ...

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution ...

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential ...

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-...

Network embedding learns the low-dimensional representations for vertices, while preserving the inter-vertex similarity reflected by the network structure. The neighborhood structure of a vertex is usually closely related with an underlying hierarchical ...

With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). ...

To have automatic conversations between human and computer is regarded as one of the most hardcore problems in computer science. Conversational systems are of growing importance due to their promising potentials and commercial values as virtual ...

Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities. Existing methods mainly rely on extracting image and question features to ...

Pattern-based methods have been successful in information extraction and NLP research. Previous approaches learn the quality of a textual pattern as relatedness to a certain task based on statistics of its individual content (e.g., length, frequency) ...

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce medical information access cost for both individuals and societies. However, expanding the scale of high-quality yet structured medical ...

Network embedding has been increasingly used in many network analytics applications to generate low-dimensional vector representations, so that many off-the-shelf models can be applied to solve a wide variety of data mining tasks. However, similar to ...

Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&EE) applications where risk management is a core component, well-...

Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation. Ideally, graph comparison should be invariant to the order of ...

This paper concerns the problem of network embedding (NE), whose aim is to learn low-dimensional representations for nodes in networks. Such dense vector representations offer great promises for many network analysis problems. However, existing NE ...

Inferring causal relationships in observational data is crucial for understanding scientific and social processes. We develop the first statistical machine learning approach for automatically discovering regression discontinuity designs (RDDs), a quasi-...

Deep learning methods have shown great success in pixel-wise prediction tasks. One of the most popular methods employs an encoder-decoder network in which deconvolutional layers are used for up-sampling feature maps. However, a key limitation of the ...