Paper Group ANR 117
Holographic Automata for Ambient Immersive A. I. via Reservoir Computing. Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging. Feature-based reformulation of entities in triple pattern queries. Transitions, Losses, and Re-parameterizations: Elements of Prediction Games. Targeted change detection in remote sensing …
Holographic Automata for Ambient Immersive A. I. via Reservoir Computing
| Title | Holographic Automata for Ambient Immersive A. I. via Reservoir Computing |
| Authors | Theophanes E. Raptis |
| Abstract | We prove the existence of a semilinear representation of Cellular Automata (CA) with the introduction of multiple convolution kernels. Examples of the technique are presented for rules akin to the “edge-of-chaos” including the Turing universal rule 110 for further utilization in the area of reservoir computing. We also examine the significance of their dual representation on a frequency or wavelength domain as a superposition of plane waves for distributed computing applications including a new proposal for a “Hologrid” that could be realized with present Wi-Fi,Li-Fi technologies. |
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| Published | 2018-06-09 |
| URL | http://arxiv.org/abs/1806.05108v2 |
| http://arxiv.org/pdf/1806.05108v2.pdf | |
| PWC | https://paperswithcode.com/paper/holographic-automata-for-ambient-immersive-a |
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Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
| Title | Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging |
| Authors | Jinhui Tang, Xiangbo Shu, Zechao Li, Yu-Gang Jiang, Qi Tian |
| Abstract | Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph. However, such methods will become computationally infeasible with the rapidly increasing number of images, tags and users. It has been proven that Anchor Graph Regularization (AGR) can significantly accelerate large-scale graph learning model by exploring only a small number of anchor points. Inspired by this, we propose a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to effectively refine the tags of social images, which is insensitive to the scale of the applied data. First, we construct an anchor-unit graph across multiple domains (e.g., image and user domains) rather than traditional anchor graph in a single domain. Second, a tensor completion based on SUGAR is implemented on the original image-tag-user tensor to refine the tags of the anchor images. Third, we efficiently assign tags to non-anchor images by leveraging the relationship between the non-anchor images and the anchor units. Experimental results on a real-world social image database well demonstrate the effectiveness of SUGAR-TC, outperforming several related methods. |
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| Published | 2018-04-12 |
| URL | http://arxiv.org/abs/1804.04397v2 |
| http://arxiv.org/pdf/1804.04397v2.pdf | |
| PWC | https://paperswithcode.com/paper/social-anchor-unit-graph-regularized-tensor |
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Feature-based reformulation of entities in triple pattern queries
| Title | Feature-based reformulation of entities in triple pattern queries |
| Authors | Amar Viswanathan, Geeth de Mel, James A. Hendler |
| Abstract | Knowledge graphs encode uniquely identifiable entities to other entities or literal values by means of relationships, thus enabling semantically rich querying over the stored data. Typically, the semantics of such queries are often crisp thereby resulting in crisp answers. Query log statistics show that a majority of the queries issued to knowledge graphs are often entity centric queries. When a user needs additional answers the state-of-the-art in assisting users is to rewrite the original query resulting in a set of approximations. Several strategies have been proposed in past to address this. They typically move up the taxonomy to relax a specific element to a more generic element. Entities don’t have a taxonomy and they end up being generalized. To address this issue, in this paper, we propose an entity centric reformulation strategy that utilizes schema information and entity features present in the graph to suggest rewrites. Once the features are identified, the entity in concern is reformulated as a set of features. Since entities can have a large number of features, we introduce strategies that select the top-k most relevant and {informative ranked features and augment them to the original query to create a valid reformulation. We then evaluate our approach by showing that our reformulation strategy produces results that are more informative when compared with state-of-the-art |
| Tasks | Knowledge Graphs |
| Published | 2018-07-04 |
| URL | http://arxiv.org/abs/1807.01801v1 |
| http://arxiv.org/pdf/1807.01801v1.pdf | |
| PWC | https://paperswithcode.com/paper/feature-based-reformulation-of-entities-in |
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Transitions, Losses, and Re-parameterizations: Elements of Prediction Games
| Title | Transitions, Losses, and Re-parameterizations: Elements of Prediction Games |
| Authors | Parameswaran Kamalaruban |
| Abstract | This thesis presents some geometric insights into three different types of two player prediction games – namely general learning task, prediction with expert advice, and online convex optimization. These games differ in the nature of the opponent (stochastic, adversarial, or intermediate), the order of the players’ move, and the utility function. The insights shed some light on the understanding of the intrinsic barriers of the prediction problems and the design of computationally efficient learning algorithms with strong theoretical guarantees (such as generalizability, statistical consistency, and constant regret etc.). |
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| Published | 2018-05-20 |
| URL | http://arxiv.org/abs/1805.08622v1 |
| http://arxiv.org/pdf/1805.08622v1.pdf | |
| PWC | https://paperswithcode.com/paper/transitions-losses-and-re-parameterizations |
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Targeted change detection in remote sensing images
| Title | Targeted change detection in remote sensing images |
| Authors | Vladimir Ignatiev, Alexey Trekin, Viktor Lobachev, Georgy Potapov, Evgeny Burnaev |
| Abstract | Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for. |
| Tasks | Object Classification |
| Published | 2018-03-14 |
| URL | http://arxiv.org/abs/1803.05482v1 |
| http://arxiv.org/pdf/1803.05482v1.pdf | |
| PWC | https://paperswithcode.com/paper/targeted-change-detection-in-remote-sensing |
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Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
| Title | Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification |
| Authors | Tao Hu, Jizheng Xu, Cong Huang, Honggang Qi, Qingming Huang, Yan Lu |
| Abstract | For fine-grained visual classification, objects usually share similar geometric structure but present variant local appearance and different pose. Therefore, localizing and extracting discriminative local features play a crucial role in accurate category prediction. Existing works either pay attention to limited object parts or train isolated networks for locating and classification. In this paper, we propose Weakly Supervised Bilinear Attention Network (WS-BAN) to solve these issues. It jointly generates a set of attention maps (region-of-interest maps) to indicate the locations of object’s parts and extracts sequential part features by Bilinear Attention Pooling (BAP). Besides, we propose attention regularization and attention dropout to weakly supervise the generating process of attention maps. WS-BAN can be trained end-to-end and achieves the state-of-the-art performance on multiple fine-grained classification datasets, including CUB-200-2011, Stanford Car and FGVC-Aircraft, which demonstrated its effectiveness. |
| Tasks | Fine-Grained Image Classification |
| Published | 2018-08-06 |
| URL | http://arxiv.org/abs/1808.02152v2 |
| http://arxiv.org/pdf/1808.02152v2.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-bilinear-attention-network |
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Quantifying Uncertainties in Natural Language Processing Tasks
| Title | Quantifying Uncertainties in Natural Language Processing Tasks |
| Authors | Yijun Xiao, William Yang Wang |
| Abstract | Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks. |
| Tasks | Language Modelling, Named Entity Recognition, Sentiment Analysis |
| Published | 2018-11-18 |
| URL | http://arxiv.org/abs/1811.07253v1 |
| http://arxiv.org/pdf/1811.07253v1.pdf | |
| PWC | https://paperswithcode.com/paper/quantifying-uncertainties-in-natural-language |
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A dataset for benchmarking vision-based localization at intersections
| Title | A dataset for benchmarking vision-based localization at intersections |
| Authors | Augusto L. Ballardini, Daniele Cattaneo, Domenico G. Sorrenti |
| Abstract | In this report we present the work performed in order to build a dataset for benchmarking vision-based localization at intersections, i.e., a set of stereo video sequences taken from a road vehicle that is approaching an intersection, altogether with a reliable measure of the observer position. This report is meant to complement our paper “Vision-Based Localization at Intersections using Digital Maps” submitted to ICRA2019. It complements the paper because the paper uses the dataset, but it had no space for describing the work done to obtain it. Moreover, the dataset is of interest for all those tackling the task of online localization at intersections for road vehicles, e.g., for a quantitative comparison with the proposal in our submitted paper, and it is therefore appropriate to put the dataset description in a separate report. We considered all datasets from road vehicles that we could find as for the end of August 2018. After our evaluation, we kept only sub-sequences from the KITTI dataset. In the future we will increase the collection of sequences with data from our vehicle. |
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| Published | 2018-11-04 |
| URL | http://arxiv.org/abs/1811.01306v1 |
| http://arxiv.org/pdf/1811.01306v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-dataset-for-benchmarking-vision-based |
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Discovering Influential Factors in Variational Autoencoder
| Title | Discovering Influential Factors in Variational Autoencoder |
| Authors | Shiqi Liu, Jingxin Liu, Qian Zhao, Xiangyong Cao, Huibin Li, Hongying Meng, Sheng Liu, Deyu Meng |
| Abstract | In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks. In this work, we focus on supervising the influential factors extracted by the variational autoencoder(VAE). The VAE is proposed to learn independent low dimension representation while facing the problem that sometimes pre-set factors are ignored. We argue that the mutual information of the input and each learned factor of the representation plays a necessary indicator of discovering the influential factors. We find the VAE objective inclines to induce mutual information sparsity in factor dimension over the data intrinsic dimension and results in some non-influential factors whose function on data reconstruction could be ignored. We show mutual information also influences the lower bound of VAE’s reconstruction error and downstream classification task. To make such indicator applicable, we design an algorithm for calculating the mutual information for VAE and prove its consistency. Experimental results on MNIST, CelebA and DEAP datasets show that mutual information can help determine influential factors, of which some are interpretable and can be used to further generation and classification tasks, and help discover the variant that connects with emotion on DEAP dataset. |
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| Published | 2018-09-06 |
| URL | http://arxiv.org/abs/1809.01804v2 |
| http://arxiv.org/pdf/1809.01804v2.pdf | |
| PWC | https://paperswithcode.com/paper/discovering-influential-factors-in |
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SdcNet: A Computation-Efficient CNN for Object Recognition
| Title | SdcNet: A Computation-Efficient CNN for Object Recognition |
| Authors | Yunlong Ma, Chunyan Wang |
| Abstract | Extracting features from a huge amount of data for object recognition is a challenging task. Convolution neural network can be used to meet the challenge, but it often requires a large number of computation resources. In this paper, a computation-efficient convolutional module, named SdcBlock, is proposed and based on it, the convolution network SdcNet is introduced for object recognition tasks. In the proposed module, optimized successive depthwise convolutions supported by appropriate data management is applied in order to generate vectors containing high density and more varieties of feature information. The hyperparameters can be easily adjusted to suit varieties of tasks under different computation restrictions without significantly jeopardizing the performance. The experiments have shown that SdcNet achieved an error rate of 5.60% in CIFAR-10 with only 55M Flops and also reduced further the error rate to 5.24% using a moderate volume of 103M Flops. The expected computation efficiency of the SdcNet has been confirmed. |
| Tasks | Object Recognition |
| Published | 2018-05-03 |
| URL | http://arxiv.org/abs/1805.01317v2 |
| http://arxiv.org/pdf/1805.01317v2.pdf | |
| PWC | https://paperswithcode.com/paper/sdcnet-a-computation-efficient-cnn-for-object |
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Towards Automated Let’s Play Commentary
| Title | Towards Automated Let’s Play Commentary |
| Authors | Matthew Guzdial, Shukan Shah, Mark Riedl |
| Abstract | We introduce the problem of generating Let’s Play-style commentary of gameplay video via machine learning. We propose an analysis of Let’s Play commentary and a framework for building such a system. To test this framework we build an initial, naive implementation, which we use to interrogate the assumptions of the framework. We demonstrate promising results towards future Let’s Play commentary generation. |
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| Published | 2018-09-25 |
| URL | http://arxiv.org/abs/1809.09424v1 |
| http://arxiv.org/pdf/1809.09424v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-automated-lets-play-commentary |
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Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model
| Title | Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model |
| Authors | Gabriele Partel, Giorgia Milli, Carolina Wählby |
| Abstract | Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probability at the cycle level is self-learned using a convolutional neural network. Signal candidates and probability predictions are thereafter fed into a graphical model searching for signal candidates across sequencing cycles. The graphical model combines intensity, probability and spatial distance to find optimal paths representing decoded signal sequences. We evaluate our approach in relation to state-of-the-art, and show that we increase recall by $27%$ at maintained sensitivity. Furthermore, visual examination shows that most of the now correctly resolved signals were previously lost due to high signal density. Thus, the proposed approach has the potential to significantly improve further analysis of spatial statistics in in situ sequencing experiments. |
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| Published | 2018-02-24 |
| URL | http://arxiv.org/abs/1802.08894v1 |
| http://arxiv.org/pdf/1802.08894v1.pdf | |
| PWC | https://paperswithcode.com/paper/improving-recall-of-in-situ-sequencing-by |
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Face recognition for monitoring operator shift in railways
| Title | Face recognition for monitoring operator shift in railways |
| Authors | S Ritika, Dattaraj Rao |
| Abstract | Train Pilot is a very tedious and stressful job. Pilots must be vigilant at all times and its easy for them to lose track of time of shift. In countries like USA the pilots are mandated by law to adhere to 8 hour shifts. If they exceed 8 hours of shift the railroads may be penalized for over-tiring their drivers. The problem happens when the 8 hour shift may end in middle of a journey. In such case, the new drivers must be moved to the location locomotive is operating for shift change. Hence accurate monitoring of drivers during their shift and making sure the shifts are scheduled correctly is very important for railroads. Here we propose an automated camera system that uses camera mounted inside Locomotive cabs to continuously record video feeds. These feeds are analyzed in real time to detect the face of driver and recognize the driver using state of the art deep Learning techniques. The outcome is an increased safety of train pilots. Cameras continuously capture video from inside the cab which is stored on an on board data acquisition device. Using advanced computer vision and deep learning techniques the videos are analyzed at regular intervals to detect presence of the pilot and identify the pilot. Using a time based analysis, it is identified for how long that shift has been active. If this time exceeds allocated shift time an alert is sent to the dispatch to adjust shift hours. |
| Tasks | Face Recognition |
| Published | 2018-02-05 |
| URL | http://arxiv.org/abs/1802.01273v2 |
| http://arxiv.org/pdf/1802.01273v2.pdf | |
| PWC | https://paperswithcode.com/paper/face-recognition-for-monitoring-operator |
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A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies
| Title | A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies |
| Authors | David Carral, Cristina Feier, Pascal Hitzler |
| Abstract | Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm. In this paper, we define a novel acyclicity notion which provides a sufficient condition for termination of the restricted chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the existing acyclicity conditions (both theoretically and empirically). Furthermore, this new acyclicity notion gives rise to a very efficient reasoning procedure. We provide evidence for this by providing a materialization based reasoner for acyclic ontologies which outperforms other state-of-the-art systems. |
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| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07274v1 |
| http://arxiv.org/pdf/1804.07274v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-practical-acyclicity-notion-for-query |
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Complexity Theory for Discrete Black-Box Optimization Heuristics
| Title | Complexity Theory for Discrete Black-Box Optimization Heuristics |
| Authors | Carola Doerr |
| Abstract | A predominant topic in the theory of evolutionary algorithms and, more generally, theory of randomized black-box optimization techniques is running time analysis. Running time analysis aims at understanding the performance of a given heuristic on a given problem by bounding the number of function evaluations that are needed by the heuristic to identify a solution of a desired quality. As in general algorithms theory, this running time perspective is most useful when it is complemented by a meaningful complexity theory that studies the limits of algorithmic solutions. In the context of discrete black-box optimization, several black-box complexity models have been developed to analyze the best possible performance that a black-box optimization algorithm can achieve on a given problem. The models differ in the classes of algorithms to which these lower bounds apply. This way, black-box complexity contributes to a better understanding of how certain algorithmic choices (such as the amount of memory used by a heuristic, its selective pressure, or properties of the strategies that it uses to create new solution candidates) influences performance. In this chapter we review the different black-box complexity models that have been proposed in the literature, survey the bounds that have been obtained for these models, and discuss how the interplay of running time analysis and black-box complexity can inspire new algorithmic solutions to well-researched problems in evolutionary computation. We also discuss in this chapter several interesting open questions for future work. |
| Tasks | |
| Published | 2018-01-06 |
| URL | http://arxiv.org/abs/1801.02037v2 |
| http://arxiv.org/pdf/1801.02037v2.pdf | |
| PWC | https://paperswithcode.com/paper/complexity-theory-for-discrete-black-box |
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