In online display advertising, state-of-the-art Click Through Rate(CTR) prediction algorithms rely heavily on historical information, and they work poorly on growing number of new ads without any historical information. This is known as the the cold start problem. For image ads, current state-of-the-art systems use handcrafted image features such as multimedia features and SIFT features to capture the attractiveness of ads. However, these handcrafted features are task dependent, inflexible and heuristic. In order to tackle the cold start problem in image display ads, we propose a new feature learning architecture to learn the most discriminative image features directly from raw pixels and user feedback in the target task. The proposed method is flexible and does not depend on human heuristic. Extensive experiments on a real world dataset with 47 billion records show that our feature learning method outperforms existing handcrafted features significantly, and it can extract discriminative and meaningful features.

Generating a coherent synopsis and revealing the development threads for news stories from the increasing amounts of news content remains aformidable challenge. In this paper, we proposed a hddCRP (hybird distant-dependent ChineseRestaurant Process) based HierARChical tOpic model for news Article cLustering, abbreviated as CHARCOAL. Given a bunch of news articles, the outcome of CHARCOAL is threefold: 1) it aggregates relevant new articles into clusters (i.e., stories); 2) it disentangles the chain links (i.e., storyline) between articles in their describing story; 3) it discerns the topics that each story is assigned (e.g., Malaysia Airlines Flight 370 story belongs to the aircraft accident topic and U.S presidential election stories belong to the politics topic). CHARCOAL completes this task by utilizing a hddCRP as prior, and the entities (e.g., names of persons, organizations, or locations) that appear in news articles as clues. Moveover, the adaptation of nonparametric nature in CHARCOAL makes our model can adaptively learn the appropriate number of stories and topics from news corpus. The experimental analysis and results demonstrate both interpretability and superiority of the proposed approach.

This paper studies the complexity of model checking multiagent systems, in particular systems succinctly described by two practical representations: concurrent representation and symbolic representation. The logics we concern include branching time temporal logics and several variants of alternating time temporal logics.

The task of machine condition monitoring is to detect machine failures at an early stage such that maintenance can be carried out in a timely manner. Most existing techniques are supervised approaches: they require user annotated training data to learn normal and faulty behaviors of a machine. However, such supervision can be difficult to acquire. In contrast, unsupervised methods don't need much human involvement, however, they face another challenge: how to model the generative (observation) process of sensor signals. We propose an unsupervised approach based on segmental hidden Markov models. Our method has a unifying observation model integrating three pieces of information that are complementary to each other. First, we model the signal as an explicit function over time, which describes its possible non-stationary trending patterns. Second, the stationary part of the signal is fit by an autoregressive model. Third, we introduce contextual information to break down the signal complexity such that the signal is modeled separately under different conditions. The advantages of the proposed model are demonstrated by tests on gas turbine, truck and honeybee datasets.

This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on  the Opportunity Activity Recognition Challenge and other  benchmark datasets.

We set out to challenge the understanding that it is difficult My work is an integrated interdisciplinary effort which employs to study the complex processing of natural stories. We used functional neuroimaging, and revolves around the development functional Magnetic Resonance Imaging (fMRI) to record the of machine learning methods to uncover multilayer brain activity of subjects while they read an unmodified chapter cognitive processes from brain activity recordings. of a popular book. Unprecedently, we modeled the measured Studying how the human brain represents meaning is not brain activity as a function of the content of the text only important for expanding our scientific knowledge of the being read Wehbe et al. [2014a]. Our model is able to extrapolate brain and of intelligence. By mapping behavioral traits to differences to predict brain activity for novel passages of text - in brain representations, we increase our understanding beyond those on which it has been trained.

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have many important applications and over the past decade, a wide variety of techniques have been studied for building such models. In this extended abstract of our 2014 AI Journal article of the same title, we summarize existing models and describe new model families and various extensions. In a comprehensive empirical analyis using 11 algorithms and 35 instance distributions spanning a wide range of hard combinatorial problems, we demonstrate that our new models yield substantially better runtime predictions than previous approaches in terms of their generalization to new problem instances, to new algorithms from a parameterized space, and to both simultaneously.

Cross-modal hashing is designed to facilitate fast search across domains. In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains. Unlike previous approaches that separate the optimization of the quantizer independent of maximization of domain correlation, our approach simultaneously optimizes both processes. The underlying relation between the domains that describes the same objects is established via maximizing the correlation between the hash codes across the domains. The resulting multi-modal objective function is transformed to a unimodal formalization, which is optimized through an alternative procedure. Experimental results on three real world datasets demonstrate that our approach outperforms the state-of-the-art multi-modal hashing methods.

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.

In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graph-views for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.