Paper Group ANR 505
Deep Loopy Neural Network Model for Graph Structured Data Representation Learning. Beyond “How may I help you?": Assisting Customer Service Agents with Proactive Responses. JobComposer: Career Path Optimization via Multicriteria Utility Learning. Dynamics and Reachability of Learning Tasks. Learning Robust Representations for Automatic Target Recog …
Deep Loopy Neural Network Model for Graph Structured Data Representation Learning
| Title | Deep Loopy Neural Network Model for Graph Structured Data Representation Learning |
| Authors | Jiawei Zhang |
| Abstract | Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data. |
| Tasks | Representation Learning |
| Published | 2018-05-19 |
| URL | https://arxiv.org/abs/1805.07504v2 |
| https://arxiv.org/pdf/1805.07504v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-loopy-neural-network-model-for-graph |
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Beyond “How may I help you?": Assisting Customer Service Agents with Proactive Responses
| Title | Beyond “How may I help you?": Assisting Customer Service Agents with Proactive Responses |
| Authors | Mengting Wan, Xin Chen |
| Abstract | We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems. Smart-reply systems have been widely applied in real-world applications (e.g. Gmail, LinkedIn Messaging), where most of them can successfully recommend reactive responses. However, we observe a major limitation of current methods is that they generally have difficulties in suggesting proactive investigation act (e.g. “Do you perhaps have another account with us?") due to the lack of long-term context information, which indeed act as critical steps for customer service agents to collect information and resolve customers’ issues. Thus in this work, we propose an end-to-end method with special focus on suggesting proactive investigative questions to customer agents in Airbnb’s customer service live-chat system. Effectiveness of our proposed method can be validated through qualitative and quantitative results. |
| Tasks | |
| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.10686v1 |
| http://arxiv.org/pdf/1811.10686v1.pdf | |
| PWC | https://paperswithcode.com/paper/beyond-how-may-i-help-you-assisting-customer |
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JobComposer: Career Path Optimization via Multicriteria Utility Learning
| Title | JobComposer: Career Path Optimization via Multicriteria Utility Learning |
| Authors | Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo |
| Abstract | With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.) becoming popular on the web, people are now turning to these platforms to create and share their professional profiles, to connect with others who share similar professional aspirations and to explore new career opportunities. These platforms however do not offer a long-term roadmap to guide career progression and improve workforce employability. The career trajectories of OPN users can serve as a reference but they are not always optimal. A career plan can also be devised through consultation with career coaches, whose knowledge may however be limited to a few industries. To address the above limitations, we present a novel data-driven approach dubbed JobComposer to automate career path planning and optimization. Its key premise is that the observed career trajectories in OPNs may not necessarily be optimal, and can be improved by learning to maximize the sum of payoffs attainable by following a career path. At its heart, JobComposer features a decomposition-based multicriteria utility learning procedure to achieve the best tradeoff among different payoff criteria in career path planning. Extensive studies using a city state-based OPN dataset demonstrate that JobComposer returns career paths better than other baseline methods and the actual career paths. |
| Tasks | |
| Published | 2018-09-04 |
| URL | http://arxiv.org/abs/1809.01062v1 |
| http://arxiv.org/pdf/1809.01062v1.pdf | |
| PWC | https://paperswithcode.com/paper/jobcomposer-career-path-optimization-via |
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Dynamics and Reachability of Learning Tasks
| Title | Dynamics and Reachability of Learning Tasks |
| Authors | Alessandro Achille, Glen Mbeng, Stefano Soatto |
| Abstract | We compute the transition probability between two learning tasks, and show that it decomposes into two factors. The first depends on the geometry of the loss landscape of a model trained on each task, independent of any particular model used. This is related to an information theoretic distance function, but is insufficient to predict success in transfer learning, as nearby tasks can be unreachable via fine-tuning. The second factor depends on the ease of traversing the path between two tasks. With this dynamic component, we derive strict lower bounds on the complexity necessary to learn a task starting from the solution to another, which is one of the most common forms of transfer learning. |
| Tasks | Semantic Textual Similarity, Transfer Learning |
| Published | 2018-10-04 |
| URL | https://arxiv.org/abs/1810.02440v2 |
| https://arxiv.org/pdf/1810.02440v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-dynamics-of-differential-learning-i |
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Learning Robust Representations for Automatic Target Recognition
| Title | Learning Robust Representations for Automatic Target Recognition |
| Authors | Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian, Sung-Hyun Son |
| Abstract | Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate different techniques for building robust classification architectures exploiting learned physical structure in received synthetic aperture radar signals of simulated 3D targets. |
| Tasks | |
| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.10714v1 |
| http://arxiv.org/pdf/1811.10714v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-robust-representations-for-automatic |
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Vicious Circle Principle and Logic Programs with Aggregates
| Title | Vicious Circle Principle and Logic Programs with Aggregates |
| Authors | Michael Gelfond, Yuanlin Zhang |
| Abstract | The paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches. |
| Tasks | |
| Published | 2018-08-21 |
| URL | http://arxiv.org/abs/1808.07050v1 |
| http://arxiv.org/pdf/1808.07050v1.pdf | |
| PWC | https://paperswithcode.com/paper/vicious-circle-principle-and-logic-programs |
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Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
| Title | Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps |
| Authors | Simon S. Du, Surbhi Goel |
| Abstract | We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models. |
| Tasks | |
| Published | 2018-05-20 |
| URL | http://arxiv.org/abs/1805.07798v2 |
| http://arxiv.org/pdf/1805.07798v2.pdf | |
| PWC | https://paperswithcode.com/paper/improved-learning-of-one-hidden-layer |
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Higher-order Projected Power Iterations for Scalable Multi-Matching
| Title | Higher-order Projected Power Iterations for Scalable Multi-Matching |
| Authors | Florian Bernard, Johan Thunberg, Paul Swoboda, Christian Theobalt |
| Abstract | The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple (NP-hard) quadratic assignment problems (QAPs) that are coupled via cycle-consistency constraints. The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their (relatively) high computational cost. We address these shortcomings by introducing a Higher-order Projected Power Iteration method, which is (i) efficient and scales to tens of thousands of points, (ii) straightforward to implement, (iii) able to incorporate geometric consistency, (iv) guarantees cycle-consistent multi-matchings, and (iv) comes with theoretical convergence guarantees. Experimentally we show that our approach is superior to existing methods. |
| Tasks | |
| Published | 2018-11-26 |
| URL | http://arxiv.org/abs/1811.10541v2 |
| http://arxiv.org/pdf/1811.10541v2.pdf | |
| PWC | https://paperswithcode.com/paper/higher-order-projected-power-iterations-for |
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Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
| Title | Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm |
| Authors | Md. Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan, Rezoana Bente Arif, Zahidun Ashrafi |
| Abstract | In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them. |
| Tasks | Image Compression, Speech Recognition, Stock Market Prediction |
| Published | 2018-09-17 |
| URL | http://arxiv.org/abs/1809.06188v3 |
| http://arxiv.org/pdf/1809.06188v3.pdf | |
| PWC | https://paperswithcode.com/paper/study-and-observation-of-the-variations-of |
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Representing Images in 200 Bytes: Compression via Triangulation
| Title | Representing Images in 200 Bytes: Compression via Triangulation |
| Authors | David Marwood, Pascal Massimino, Michele Covell, Shumeet Baluja |
| Abstract | A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page load process to improve responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore an active research direction. In this work, we concentrate on extreme compression rates, where the size of the image is typically 200 bytes or less. First, we propose a novel approach for image compression that, unlike commonly used methods, does not rely on block-based statistics. We use an approach based on an adaptive triangulation of the target image, devoting more triangles to high entropy regions of the image. Second, we present a novel algorithm for encoding the triangles. The results show favorable statistics, in terms of PSNR and SSIM, over both the JPEG and the WebP standards. |
| Tasks | Image Compression |
| Published | 2018-09-07 |
| URL | http://arxiv.org/abs/1809.02257v2 |
| http://arxiv.org/pdf/1809.02257v2.pdf | |
| PWC | https://paperswithcode.com/paper/representing-images-in-200-bytes-compression |
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Analyzing and learning the language for different types of harassment
| Title | Analyzing and learning the language for different types of harassment |
| Authors | Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth |
| Abstract | Disclaimer: This paper is concerned with violent online harassment. To describe the subject at an adequate level of realism, examples of our collected tweets involve violent, threatening, vulgar and hateful speech language in the context of racial, sexual, political, appearance and intellectual harassment. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires that we can identify different forms or types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the context that represents the interrelated conditions in which they occur. In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. To study the context for each type that sheds light on the linguistic meaning, interpretation, and distribution, we conduct two lines of investigation: an extensive linguistic analysis, and a statistical distribution of unigrams. We then build type-ware classifiers to automate the identification of type-specific harassment. Our experiments demonstrate that these classifiers provide competitive accuracy for identifying and analyzing harassment on social media. We present extensive discussion and major observations about the effectiveness of type-aware classifiers using a detailed comparison setup providing insight into the role of type-dependent features. |
| Tasks | |
| Published | 2018-11-01 |
| URL | https://arxiv.org/abs/1811.00644v2 |
| https://arxiv.org/pdf/1811.00644v2.pdf | |
| PWC | https://paperswithcode.com/paper/analyzing-and-learning-the-language-for |
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ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks
| Title | ExGate: Externally Controlled Gating for Feature-based Attention in Artificial Neural Networks |
| Authors | Jarryd Son, Amit Mishra |
| Abstract | Perceptual capabilities of artificial systems have come a long way since the advent of deep learning. These methods have proven to be effective, however they are not as efficient as their biological counterparts. Visual attention is a set of mechanisms that are employed in biological visual systems to ease computational load by only processing pertinent parts of the stimuli. This paper addresses the implementation of top-down, feature-based attention in an artificial neural network by use of externally controlled neuron gating. Our results showed a 5% increase in classification accuracy on the CIFAR-10 dataset versus a non-gated version, while adding very few parameters. Our gated model also produces more reasonable errors in predictions by drastically reducing prediction of classes that belong to a different category to the true class. |
| Tasks | |
| Published | 2018-11-08 |
| URL | http://arxiv.org/abs/1811.03403v1 |
| http://arxiv.org/pdf/1811.03403v1.pdf | |
| PWC | https://paperswithcode.com/paper/exgate-externally-controlled-gating-for |
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Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System
| Title | Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System |
| Authors | Brian Reinhardt, Isaac Gerg, Daniel Brown, Joonho Park |
| Abstract | Performance of automatic target recognition from synthetic aperture sonar data is heavily dependent on the complexity of the beamformed imagery. Several mechanisms can contribute to this, including unwanted vehicle dynamics, the bathymetry of the scene, and the presence of natural and manmade clutter. To understand the impact of the environmental complexity on image perception, researchers have taken approaches rooted in information theory, or heuristics. Despite these efforts, a quantitative measure for complexity has not been related to the phenomenology from which it is derived. By using subject matter experts (SMEs) we derive a complexity metric for a set of imagery which accounts for the underlying phenomenology. The goal of this work is to develop an understanding of how several common information theoretic and heuristic measures are related to the SME perceived complexity in synthetic aperture sonar imagery. To achieve this, an ensemble of 10-meter x 10-meter images were cropped from a high-frequency SAS data set that spans multiple environments. The SME’s were presented pairs of images from which they could rate the relative image complexity. These comparisons were then converted into the desired sequential ranking using a method first developed by A. Elo for establishing rankings of chess players. The Elo method produced a plausible rank ordering across the broad dataset. The heuristic and information theoretical metrics were then compared to the image rank from which they were derived. The metrics with the highest degree of correlation were those relating to spatial information, e.g. variations in pixel intensity, with an R-squared value of approximately 0.9. However, this agreement was dependent on the scale from which the spatial variation was measured. Results will also be presented for many other measures including lacunarity, image compression, and entropy. |
| Tasks | Image Compression |
| Published | 2018-08-15 |
| URL | http://arxiv.org/abs/1808.05279v1 |
| http://arxiv.org/pdf/1808.05279v1.pdf | |
| PWC | https://paperswithcode.com/paper/measuring-human-assessed-complexity-in |
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Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
| Title | Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules |
| Authors | Boris Knyazev, Xiao Lin, Mohamed R. Amer, Graham W. Taylor |
| Abstract | Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Chebyshev GCNs restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways to fuse the representations extracted from annotated and learned edges, achieving competitive results on a variety of chemical classification benchmarks. |
| Tasks | Graph Classification, Node Classification |
| Published | 2018-11-23 |
| URL | http://arxiv.org/abs/1811.09595v1 |
| http://arxiv.org/pdf/1811.09595v1.pdf | |
| PWC | https://paperswithcode.com/paper/spectral-multigraph-networks-for-discovering |
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Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
| Title | Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search |
| Authors | Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, Kurt Keutzer |
| Abstract | Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources. However, existing quantization methods often represent all weights and activations with the same precision (bit-width). In this paper, we explore a new dimension of the design space: quantizing different layers with different bit-widths. We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search (DNAS) framework to efficiently explore its exponential search space with gradient-based optimization. Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet. Our quantized models with 21.1x smaller model size or 103.9x lower computational cost can still outperform baseline quantized or even full precision models. |
| Tasks | Neural Architecture Search, Quantization |
| Published | 2018-11-30 |
| URL | http://arxiv.org/abs/1812.00090v1 |
| http://arxiv.org/pdf/1812.00090v1.pdf | |
| PWC | https://paperswithcode.com/paper/mixed-precision-quantization-of-convnets-via |
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