Query: Factor-based Compositional Embedding Models

Relevant passages:
Natural Language Processing (almost) from Scratch <=SEP=> We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

Deep Learning for Efficient Discriminative Parsing <=SEP=> We propose a new fast purely discriminative algorithm for natural language parsing, based on a “deep” recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of “levels”, the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features which leverage word representations from Collobert and Weston (2008), we show similar performance (in F1 score) to existing pure discriminative parsers and existing “benchmark” parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.

Distributed and overlapping representations of faces and objects in ventral temporal cortex. <=SEP=> The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.

A latent factor model for highly multi-relational data <=SEP=> Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi-relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.

Learning representations by back-propagating errors <=SEP=> We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

A memory insensitive technique for large model simplification <=SEP=> In this paper we propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of Lindstrom [20] which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems.The original OoCS algorithm has memory complexity that depends on the size of the output mesh, but no dependency on the size of the input mesh. That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm, the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses.Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.

SecondNet: a data center network virtualization architecture with bandwidth guarantees <=SEP=> In this paper, we propose virtual data center (VDC) as the unit of resource allocation for multiple tenants in the cloud. VDCs are more desirable than physical data centers because the resources allocated to VDCs can be rapidly adjusted as tenants' needs change. To enable the VDC abstraction, we design a data center network virtualization architecture called SecondNet. SecondNet achieves scalability by distributing all the virtual-to-physical mapping, routing, and bandwidth reservation state in server hypervisors. Its port-switching based source routing (PSSR) further makes SecondNet applicable to arbitrary network topologies using commodity servers and switches. SecondNet introduces a centralized VDC allocation algorithm for bandwidth guaranteed virtual to physical mapping. Simulations demonstrate that our VDC allocation achieves high network utilization and low time complexity. Our implementation and experiments show that we can build SecondNet on top of various network topologies, and SecondNet provides bandwidth guarantee and elasticity, as designed.

A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology <=SEP=> We consider the problem of identifying the patients who are diagnosed with highgrade prostate cancer using the histopathology of tumor in a prostate needle biopsy and are at a very high risk of lethal cancer progression. We hypothesize that the morphology of tumor cell nuclei in digital images from the biopsy can be used to predict tumor aggressiveness and posit the presence of metastasis as a surrogate for disease specific mortality. For this purpose, we apply a compositional multiinstance learning approach which encodes images of nuclei through a convolutional neural network, then predicts the presence of metastasis from sets of encoded nuclei. Through experiments on prostate needle biopsies (PNBX) from a patient cohort with known presence (M1 stage, n = 85) or absence (M0 stage, n = 86) of metastatic disease, we obtained an average area under the receiver operating characteristic curve of 0.71± 0.08 for predicting metastatic cases. These results support our hypothesis that information related to metastatic capacity of prostate cancer cells can be obtained through analysis of nuclei and establish a baseline for future research aimed at predicting the risk of future metastatic disease at a time when it might be preventable.

Design of a dual circular polarization microstrip patch array antenna <=SEP=> Design of a microstrip array antenna to achieve dual circular polarization is proposed in this paper. The proposed antenna is a 2×2 array antenna where each patch element is circularly polarized. The feed network has microstrip lines, cross slot lines and air-bridges. The array antenna can excite both right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) without using any 90° hybrid circuit or PIN diode. “Both-sided MIC Technology” is used to design the feed network as it provides flexibility to place several types of transmission lines on both sides of the dielectric substrate. The design frequency of the proposed array antenna is 10 GHz. The simulated return loss exhibits an impedance bandwidth of greater than 5% and the 3-dB axial ratio bandwidths for both RHCP and LHCP are approximately 1.39%. The structure and the basic operation along with the simulation results of the proposed dual circularly polarized array antenna are demonstrated in this paper.

Matching Visual Saliency to Confidence in Plots of Uncertain Data <=SEP=> Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.

Lightweight, High-Force Gripper Inspired by Chuck Clamping Devices <=SEP=> In this letter, we present a novel gripper, whose design was inspired by chuck clamping devices, for transferring heavy objects and assembling parts precisely in industrial applications. The developed gripper is lightweight (0.9 kg), can manipulate heavy payloads (over 23 kgf), and can automatically align its position and posture via a grasping motion. A fingertip design criterion is presented for the position alignment, while a control strategy is presented for the posture alignment. With one actuator, this gripper realized the above features. This letter describes the mathematical analyses and experiments used to validate these key metrics.

A Multiarmed Bandit Incentive Mechanism for Crowdsourcing Demand Response in Smart Grids <=SEP=> Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.

Clarifying Hypotheses by Sketching Data <=SEP=> Discussions between data analysts and colleagues or clients with no statistical background are difficult, as the analyst often has to teach and explain their statistical and domain knowledge. We investigate work practices of data analysts who collaborate with non-experts, and report findings regarding types of analysis, collaboration and availability of data. Based on these, we have created a tool to enhance collaboration between data analysts and their clients in the initial stages of the analytical process. Sketching time series data allows analysts to discuss expectations for later analysis. We propose function composition rather than freehand sketching, in order to structure the analyst-client conversation by independently expressing expected features in the data. We evaluate the usability of our prototype through two small studies, and report on user feedback for future iterations.

Calibrated fuzzy AHP for current bank account selection <=SEP=> 0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.12.089 ⇑ Corresponding author. Tel.: +44 23 92 844171. E-mail addresses: Alessio.Ishizaka@port.ac.uk (A. I com (N.H. Nguyen). Fuzzy AHP is a hybrid method that combines Fuzzy Set Theory and AHP. It has been developed to take into account uncertainty and imprecision in the evaluations. Fuzzy Set Theory requires the definition of a membership function. At present, there are no indications of how these membership functions can be constructed. In this paper, a way to calibrate the membership functions with comparisons given by the decision-maker on alternatives with known measures is proposed. This new technique is illustrated in a study measuring the most important factors in selecting a student current account. 2012 Elsevier Ltd. All rights reserved.

Intelligent Widgets for Intuitive Interaction and Coordination in Smart Home Environments <=SEP=> The intelligent home environment is a well-established example of the Ambient Intelligence application domain. A variety of sensors and actuators can be used to have the home environment adapt towards changing circumstances and user preferences. However, the complexity of how these intelligent home automation systems operate is often beyond the comprehension of non-technical users, and adding new technology to an existing infrastructure is often a burden. In this paper, we present a home automation framework designed based on smart widgets with a model driven methodology that raises the level of abstraction to configure home automation equipment. It aims to simplify user-level home automation management by mapping high-level home automation concepts onto a low-level composition and configuration of the automation building blocks with a reverse mapping to simplify the integration of new equipment into existing home automation systems. Experiments have shown that the mappings we proposed are sufficient to represent household appliances to the end user in a simple way and that new mappings can easily be added to our framework.

A Hybrid Approach to Detect and Localize Texts in Natural Scene Images <=SEP=> Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

A Pneumatic-Driven Haptic Glove with Force and Tactile Feedback <=SEP=> The advent of Oculus Rift indicates the start of a booming era of virtual reality. In order to increase the immersive feeling of interaction with the virtual world, haptic devices allow us to touch and manipulate virtual objects in an intuitive way. In this paper, we introduce a portable and low-cost haptic glove that provides both force and tactile feedback using a direct-control pneumatic concept. To produce force feedback, two inlet ports of a double acting pneumatic cylinder are opened and closed via solenoid DC valves through Pulse-width modulation (PWM) technique. For tactile feedback, an air bladder is actuated using a diaphragm pump via PWM operated solenoid valve. Experiments on a single finger prototype validated that the glove can provide force and tactile feedback with sufficient moving range of the finger joints. The maximum continuous force is 9 Newton and the response time is less than 400ms. The glove is light weighted and easy to be mounted on the index finger. The proposed glove could be potentially used for virtual reality grasping scenarios and for teleoperation of a robotic hand for handling hazardous objects.

Client-side rate adaptation scheme for HTTP adaptive streaming based on playout buffer model <=SEP=> HTTP Adaptive Streaming (HAS) is an adaptive bitrate streaming technique which is able to adapt to the network conditions using conventional HTTP web servers. An HAS player periodically requests pre-encoded video chunks by sending an HTTP GET message. When the downloading a video chunk is finished, the player estimates the network bandwidth by calculating the goodput and adjusts the video quality based on its estimates. However, the bandwidth estimation in application layer is pretty inaccurate due to its architectural limitation. We show that inaccurate bandwidth estimation in rate adaptation may incur serious rate oscillations which is poor quality-of-experience for users. In this paper, we propose a buffer-based rate adaptation scheme which eliminates the bandwidth estimation step in rate adaptation to provide a smooth playback of HTTP-based streaming. We evaluate the performance of the HAS player implemented in the ns-3 network simulator. Our simulation results show that the proposed scheme significantly improves the stability by replacing bandwidth estimation with buffer occupancy estimation.

Approximating dynamic global illumination in image space <=SEP=> Physically plausible illumination at real-time framerates is often achieved using approximations. One popular example is ambient occlusion (AO), for which very simple and efficient implementations are used extensively in production. Recent methods approximate AO between nearby geometry in screen space (SSAO). The key observation described in this paper is, that screen-space occlusion methods can be used to compute many more types of effects than just occlusion, such as directional shadows and indirect color bleeding. The proposed generalization has only a small overhead compared to classic SSAO, approximates direct and one-bounce light transport in screen space, can be combined with other methods that simulate transport for macro structures and is visually equivalent to SSAO in the worst case without introducing new artifacts. Since our method works in screen space, it does not depend on the geometric complexity. Plausible directional occlusion and indirect lighting effects can be displayed for large and fully dynamic scenes at real-time frame rates.

Development and validation of the childhood narcissism scale. <=SEP=> In this article, we describe the development and validation of a short (10 item) but comprehensive self-report measure of childhood narcissism. The Childhood Narcissism Scale (CNS) is a 1-dimensional measure of stable individual differences in childhood narcissism with strong internal consistency reliability (Studies 1-4). The CNS is virtually unrelated to conventional measures of self-esteem but is positively related to self-appraised superiority, social evaluative concern and self-esteem contingency, agentic interpersonal goals, and emotional extremity (Study 5). Furthermore, the CNS is negatively related to empathic concern and positively related to aggression following ego threat (Study 6). These results suggest that childhood narcissism has similar psychological and interpersonal correlates as adult narcissism. The CNS provides researchers a convenient tool for measuring narcissism in children and young adolescents with strong preliminary psychometric characteristics.

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis <=SEP=> Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.

Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network <=SEP=> Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5%. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.

Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data <=SEP=> Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and CV event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a CV event within 5 years. In such data, event status may be unknown for some individuals, as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modeling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with ad hoc approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large US Midwestern health care system.

Voice Activated Virtual Assistants Personality Perceptions and Desires- Comparing Personality Evaluation Frameworks <=SEP=> Currently, Voice Activated Virtual Assistants and Artificial Intelligence technologies are not just about performance or the functionalities they can carry out, it is also about the associated personality. This empirical multi-country study explores the personality perceptions of current VAVA users regarding these technologies. Since this is a rather unexplored territory for research, this study has identified two well-established personality evaluation methodologies, Aaker’s traits approach and Jung’s archetypes, to investigate current perceived personality and future desired personality of the four main Voice Activated Virtual Assistants: Siri, Google Assistant, Cortana and Alexa. Following are a summary of results by each methodology, and an analysis of the commonalities found between the two methodologies.

Conceptualizing Context for Pervasive Advertising <=SEP=> Profile-driven personalization based on socio-demographics is currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition: Advertising systems that can adapt to the situational context of a consumer will rapidly gain importance. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer’s context are largely unanswered. In this chapter, we analyze the purchase context of a retail outlet and conceptualize it such that adaptive pervasive advertising applications really deliver on their potential: showing the right message at the right time to the right recipient. full version published as: Bauer, Christine & Spiekermann, Sarah (2011). Conceptualizing Context for Pervasive Advertising. In Müller, Jörg, Alt, Florian, & Michelis, Daniel (Eds.), Pervasive Advertising (pp. 159-183). London: Springer.

Self-Soldering Connectors for Modular Robots <=SEP=> The connection mechanism between neighboring modules is the most critical subsystem of each module in a modular robot. Here, we describe a strong, lightweight, and solid-state connection method based on heating a low melting point alloy to form reversible soldered connections. No external manipulation is required for forming or breaking connections between adjacent connectors, making this method suitable for reconfigurable systems such as self-reconfiguring modular robots. Energy is only consumed when switching connectivity, and the ability to transfer power and signal through the connector is inherent to the method. Soldering connectors have no moving parts, are orders of magnitude lighter than other connectors, and are readily mass manufacturable. The mechanical strength of the connector is measured as 173 N, which is enough to support many robot modules, and hundreds of connection cycles are performed before failure.

The Numerics of GANs <=SEP=> In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

Culture and Institutions <=SEP=> A growing body of empirical work measuring different types of cultural traits has shown that culture matters for a variety of economic outcomes. This paper focuses on one specific aspect of the relevance of culture: its relationship to institutions. We review work with a theoretical, empirical, and historical bent to assess the presence of a two-way causal effect between culture and institutions. 1 We thank Benjamin Friedman and Andrei Shleifer for useful conversations and Janet Currie, Steven Durlauf, and six anonymous referees for excellent comments.

Challenges and prospects of lithium-sulfur batteries. <=SEP=> Electrical energy storage is one of the most critical needs of 21st century society. Applications that depend on electrical energy storage include portable electronics, electric vehicles, and devices for renewable energy storage from solar and wind. Lithium-ion (Li-ion) batteries have the highest energy density among the rechargeable battery chemistries. As a result, Li-ion batteries have proven successful in the portable electronics market and will play a significant role in large-scale energy storage. Over the past two decades, Li-ion batteries based on insertion cathodes have reached a cathode capacity of ∼250 mA h g(-1) and an energy density of ∼800 W h kg(-1), which do not meet the requirement of ∼500 km between charges for all-electric vehicles. With a goal of increasing energy density, researchers are pursuing alternative cathode materials such as sulfur and O2 that can offer capacities that exceed those of conventional insertion cathodes, such as LiCoO2 and LiMn2O4, by an order of magnitude (>1500 mA h g(-1)). Sulfur, one of the most abundant elements on earth, is an electrochemically active material that can accept up to two electrons per atom at ∼2.1 V vs Li/Li(+). As a result, sulfur cathode materials have a high theoretical capacity of 1675 mA h g(-1), and lithium-sulfur (Li-S) batteries have a theoretical energy density of ∼2600 W h kg(-1). Unlike conventional insertion cathode materials, sulfur undergoes a series of compositional and structural changes during cycling, which involve soluble polysulfides and insoluble sulfides. As a result, researchers have struggled with the maintenance of a stable electrode structure, full utilization of the active material, and sufficient cycle life with good system efficiency. Although researchers have made significant progress on rechargeable Li-S batteries in the last decade, these cycle life and efficiency problems prevent their use in commercial cells. To overcome these persistent problems, researchers will need new sulfur composite cathodes with favorable properties and performance and new Li-S cell configurations. In this Account, we first focus on the development of novel composite cathode materials including sulfur-carbon and sulfur-polymer composites, describing the design principles, structure and properties, and electrochemical performances of these new materials. We then cover new cell configurations with carbon interlayers and Li/dissolved polysulfide cells, emphasizing the potential of these approaches to advance capacity retention and system efficiency. Finally, we provide a brief survey of efficient electrolytes. The Account summarizes improvements that could bring Li-S technology closer to mass commercialization.

Segmentation Guided Attention Networks for Visual Question Answering <=SEP=> In this paper we propose to solve the problem of Visual Question Answering by using a novel segmentation guided attention based network which we call SegAttendNet. We use image segmentation maps, generated by a Fully Convolutional Deep Neural Network to refine our attention maps and use these refined attention maps to make the model focus on the relevant parts of the image to answer a question. The refined attention maps are used by the LSTM network to learn to produce the answer. We presently train our model on the visual7W dataset and do a category wise evaluation of the 7 question categories. We achieve state of the art results on this dataset and beat the previous benchmark on this dataset by a 1.5% margin improving the question answering accuracy from 54.1% to 55.6% and demonstrate improvements in each of the question categories. We also visualize our generated attention maps and note their improvement over the attention maps generated by the previous best approach.