Paper Group ANR 536
Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices. Decoupled Novel Object Captioner. Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash. Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary …
Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices
| Title | Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices |
| Authors | Ningquan Wang, Ruxiu Liu, Roozbeh Khodambashi, Norh Asmare, A. Fatih Sarioglu |
| Abstract | Spatial separation of suspended particles based on contrast in their physical or chemical properties forms the basis of various biological assays performed on lab-on-achip devices. To electronically acquire this information, we have recently introduced a microfluidic sensing platform, called Microfluidic CODES, which combines the resistive pulse sensing with the code division multiple access in multiplexing a network of integrated electrical sensors. In this paper, we enhance the multiplexing capacity of the Microfluidic CODES by employing sensors that generate non-orthogonal code waveforms and a new decoding algorithm that combines machine learning techniques with minimum mean-squared error estimation. As a proof of principle, we fabricated a microfluidic device with a network of 10 code-multiplexed sensors and characterized it using cells suspended in phosphate buffer saline solution. |
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| Published | 2018-08-10 |
| URL | http://arxiv.org/abs/1808.03388v1 |
| http://arxiv.org/pdf/1808.03388v1.pdf | |
| PWC | https://paperswithcode.com/paper/code-division-multiplexed-resistive-pulse |
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Decoupled Novel Object Captioner
| Title | Decoupled Novel Object Captioner |
| Authors | Yu Wu, Linchao Zhu, Lu Jiang, Yi Yang |
| Abstract | Image captioning is a challenging task where the machine automatically describes an image by sentences or phrases. It often requires a large number of paired image-sentence annotations for training. However, a pre-trained captioning model can hardly be applied to a new domain in which some novel object categories exist, i.e., the objects and their description words are unseen during model training. To correctly caption the novel object, it requires professional human workers to annotate the images by sentences with the novel words. It is labor expensive and thus limits its usage in real-world applications. In this paper, we introduce the zero-shot novel object captioning task where the machine generates descriptions without extra sentences about the novel object. To tackle the challenging problem, we propose a Decoupled Novel Object Captioner (DNOC) framework that can fully decouple the language sequence model from the object descriptions. DNOC has two components. 1) A Sequence Model with the Placeholder (SM-P) generates a sentence containing placeholders. The placeholder represents an unseen novel object. Thus, the sequence model can be decoupled from the novel object descriptions. 2) A key-value object memory built upon the freely available detection model, contains the visual information and the corresponding word for each object. The SM-P will generate a query to retrieve the words from the object memory. The placeholder will then be filled with the correct word, resulting in a caption with novel object descriptions. The experimental results on the held-out MSCOCO dataset demonstrate the ability of DNOC in describing novel concepts in the zero-shot novel object captioning task. |
| Tasks | Image Captioning |
| Published | 2018-04-11 |
| URL | http://arxiv.org/abs/1804.03803v2 |
| http://arxiv.org/pdf/1804.03803v2.pdf | |
| PWC | https://paperswithcode.com/paper/decoupled-novel-object-captioner |
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Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash
| Title | Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash |
| Authors | Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U-chupala, Yoshiki Tanaka, Yuichi Kageyama |
| Abstract | Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster. |
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| Published | 2018-11-13 |
| URL | http://arxiv.org/abs/1811.05233v2 |
| http://arxiv.org/pdf/1811.05233v2.pdf | |
| PWC | https://paperswithcode.com/paper/massively-distributed-sgd-imagenetresnet-50 |
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Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results)
| Title | Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results) |
| Authors | Anton Andreychuk, Konstantin Yakovlev |
| Abstract | We study the problem of planning collision-free paths for a group of homogeneous robots. We propose a novel approach for turning the paths that were planned egocentrically by the robots, e.g. without taking other robots’ moves into account, into collision-free trajectories and evaluate it empirically. Suggested algorithm is much faster (up to one order of magnitude) than state-of-the-art but this comes at the price of notable drop-down of the solution cost. |
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| Published | 2018-07-05 |
| URL | http://arxiv.org/abs/1807.01909v1 |
| http://arxiv.org/pdf/1807.01909v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-robot-path-planning-in-well-formed |
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Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces
| Title | Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces |
| Authors | Gellért Weisz, Paweł Budzianowski, Pei-Hao Su, Milica Gašić |
| Abstract | In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art. |
| Tasks | Spoken Dialogue Systems |
| Published | 2018-02-11 |
| URL | http://arxiv.org/abs/1802.03753v1 |
| http://arxiv.org/pdf/1802.03753v1.pdf | |
| PWC | https://paperswithcode.com/paper/sample-efficient-deep-reinforcement-learning |
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Novel Single View Constraints for Manhattan 3D Line Reconstruction
| Title | Novel Single View Constraints for Manhattan 3D Line Reconstruction |
| Authors | Siddhant Ranade, Srikumar Ramalingam |
| Abstract | This paper proposes a novel and exact method to reconstruct line-based 3D structure from a single image using Manhattan world assumption. This problem is a distinctly unsolved problem because there can be multiple 3D reconstructions from a single image. Thus, we are often forced to look for priors like Manhattan world assumption and common scene structures. In addition to the standard orthogonality, perspective projection, and parallelism constraints, we investigate a few novel constraints based on the physical realizability of the 3D scene structure. We treat the line segments in the image to be part of a graph similar to straws and connectors game, where the goal is to back-project the line segments in 3D space and while ensuring that some of these 3D line segments connect with each other (i.e., truly intersect in 3D space) to form the 3D structure. We consider three sets of novel constraints while solving the reconstruction: (1) constraints on a series of Manhattan line intersections that form cycles, but are not all physically realizable, (2) constraints on true and false intersections in the case of nearby lines lying on the same Manhattan plane, and (3) constraints from the intersections on boundary and non-boundary line segments. The reconstruction is achieved using mixed integer linear programming (MILP), and we show compelling results on real images. Along with this paper, we will release a challenging Single View Line Reconstruction dataset with ground truth 3D line models for research purposes. |
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| Published | 2018-10-08 |
| URL | http://arxiv.org/abs/1810.03737v1 |
| http://arxiv.org/pdf/1810.03737v1.pdf | |
| PWC | https://paperswithcode.com/paper/novel-single-view-constraints-for-manhattan |
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Integrating domain knowledge: using hierarchies to improve deep classifiers
| Title | Integrating domain knowledge: using hierarchies to improve deep classifiers |
| Authors | Clemens-Alexander Brust, Joachim Denzler |
| Abstract | One of the most prominent problems in machine learning in the age of deep learning is the availability of sufficiently large annotated datasets. For specific domains, e.g. animal species, a long-tail distribution means that some classes are observed and annotated insufficiently. Additional labels can be prohibitively expensive, e.g. because domain experts need to be involved. However, there is more information available that is to the best of our knowledge not exploited accordingly. In this paper, we propose to make use of preexisting class hierarchies like WordNet to integrate additional domain knowledge into classification. We encode the properties of such a class hierarchy into a probabilistic model. From there, we derive a novel label encoding and a corresponding loss function. On the ImageNet and NABirds datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy over the baseline respectively. After less than a third of training time, it is already able to match the baseline’s fine-grained recognition performance. Both results show that our suggested method is efficient and effective. |
| Tasks | Data Augmentation |
| Published | 2018-11-17 |
| URL | https://arxiv.org/abs/1811.07125v2 |
| https://arxiv.org/pdf/1811.07125v2.pdf | |
| PWC | https://paperswithcode.com/paper/integrating-domain-knowledge-using |
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A tutorial on MDL hypothesis testing for graph analysis
| Title | A tutorial on MDL hypothesis testing for graph analysis |
| Authors | Peter Bloem, Steven de Rooij |
| Abstract | This document provides a tutorial description of the use of the MDL principle in complex graph analysis. We give a brief summary of the preliminary subjects, and describe the basic principle, using the example of analysing the size of the largest clique in a graph. We also provide a discussion of how to interpret the results of such an analysis, making note of several common pitfalls. |
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| Published | 2018-10-31 |
| URL | http://arxiv.org/abs/1810.13163v1 |
| http://arxiv.org/pdf/1810.13163v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-tutorial-on-mdl-hypothesis-testing-for |
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A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments
| Title | A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments |
| Authors | Juan Luis Suárez, Salvador García, Francisco Herrera |
| Abstract | Distance metric learning is a branch of machine learning that aims to learn distances from the data. Distance metric learning can be useful to improve similarity learning algorithms, and also has applications in dimensionality reduction. This paper describes the distance metric learning problem and analyzes its main mathematical foundations. In addition, it also discusses some of the most popular distance metric learning techniques used in classification, showing their goals and the required information to understand and use them. Furthermore, some experiments to evaluate the performance of the different algorithms are also provided. Finally, this paper discusses several possibilities of future work in this topic. |
| Tasks | Dimensionality Reduction, Metric Learning |
| Published | 2018-12-14 |
| URL | https://arxiv.org/abs/1812.05944v2 |
| https://arxiv.org/pdf/1812.05944v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-tutorial-on-distance-metric-learning |
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End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
| Title | End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging |
| Authors | Gerard Snaauw, Dong Gong, Gabriel Maicas, Anton van den Hengel, Wiro J. Niessen, Johan Verjans, Gustavo Carneiro |
| Abstract | Cardiac magnetic resonance (CMR) is used extensively in the diagnosis and management of cardiovascular disease. Deep learning methods have proven to deliver segmentation results comparable to human experts in CMR imaging, but there have been no convincing results for the problem of end-to-end segmentation and diagnosis from CMR. This is in part due to a lack of sufficiently large datasets required to train robust diagnosis models. In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets. In particular, the optimisation loss is based on multi-task learning that jointly trains for the tasks of segmentation and diagnosis classification. We hypothesize that segmentation has a regularizing effect on the learning of features relevant for diagnosis. Using the 100 training and 50 testing samples available from the Automated Cardiac Diagnosis Challenge (ACDC) dataset, which has a balanced distribution of 5 cardiac diagnoses, we observe a reduction of the classification error from 32% to 22%, and a faster convergence compared to a baseline without segmentation. To the best of our knowledge, this is the best diagnosis results from CMR using an end-to-end diagnosis and segmentation learning method. |
| Tasks | Multi-Task Learning |
| Published | 2018-10-23 |
| URL | http://arxiv.org/abs/1810.10117v1 |
| http://arxiv.org/pdf/1810.10117v1.pdf | |
| PWC | https://paperswithcode.com/paper/end-to-end-diagnosis-and-segmentation |
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On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
| Title | On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage |
| Authors | Patrick Glauner, Radu State, Petko Valtchev, Diogo Duarte |
| Abstract | In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to shed light on this topic in order to increase the overall attention to this issue in the field of machine learning. We propose a scalable novel framework for reducing multiple biases in high-dimensional data sets in order to train more reliable predictors. We apply our methodology to the detection of irregular power usage from real, noisy industrial data. In emerging markets, irregular power usage, and electricity theft in particular, may range up to 40% of the total electricity distributed. Biased data sets are of particular issue in this domain. We show that reducing these biases increases the accuracy of the trained predictors. Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage. |
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| Published | 2018-01-17 |
| URL | http://arxiv.org/abs/1801.05627v2 |
| http://arxiv.org/pdf/1801.05627v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-reduction-of-biases-in-big-data-sets |
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Modeling Online Discourse with Coupled Distributed Topics
| Title | Modeling Online Discourse with Coupled Distributed Topics |
| Authors | Akshay Srivatsan, Zachary Wojtowicz, Taylor Berg-Kirkpatrick |
| Abstract | In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns. |
| Tasks | |
| Published | 2018-09-19 |
| URL | http://arxiv.org/abs/1809.07282v2 |
| http://arxiv.org/pdf/1809.07282v2.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-online-discourse-with-coupled |
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Temporal Event Knowledge Acquisition via Identifying Narratives
| Title | Temporal Event Knowledge Acquisition via Identifying Narratives |
| Authors | Wenlin Yao, Ruihong Huang |
| Abstract | Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal “before/after” event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large text corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperform several recent neural network models on the narrative cloze task. |
| Tasks | Relation Classification |
| Published | 2018-05-28 |
| URL | http://arxiv.org/abs/1805.10956v1 |
| http://arxiv.org/pdf/1805.10956v1.pdf | |
| PWC | https://paperswithcode.com/paper/temporal-event-knowledge-acquisition-via |
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Image Retargetability
| Title | Image Retargetability |
| Authors | Fan Tang, Weiming Dong, Yiping Meng, Chongyang Ma, Fuzhang Wu, Xinrui Li, Tong-Yee Lee |
| Abstract | Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem. To train and analyze this model, we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters. Experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show applications of image retargetability in retargeting method selection, retargeting method assessment and photo collage generation. |
| Tasks | |
| Published | 2018-02-12 |
| URL | https://arxiv.org/abs/1802.04392v2 |
| https://arxiv.org/pdf/1802.04392v2.pdf | |
| PWC | https://paperswithcode.com/paper/image-retargetability |
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Gender Bias in Coreference Resolution
| Title | Gender Bias in Coreference Resolution |
| Authors | Rachel Rudinger, Jason Naradowsky, Brian Leonard, Benjamin Van Durme |
| Abstract | We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these “Winogender schemas,” we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics. |
| Tasks | Coreference Resolution |
| Published | 2018-04-25 |
| URL | http://arxiv.org/abs/1804.09301v1 |
| http://arxiv.org/pdf/1804.09301v1.pdf | |
| PWC | https://paperswithcode.com/paper/gender-bias-in-coreference-resolution |
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