Paper Group ANR 77
Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network. Comparison between DeepESNs and gated RNNs on multivariate time-series prediction. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. Exploiting Explicit Paths for Multi-hop Reading Comprehension. Color naming reflects both perceptual structur …
Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network
| Title | Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network |
| Authors | Yuan Wu, Lingling Li, Lian Li |
| Abstract | We introduce the chi-square test neural network: a single hidden layer backpropagation neural network using chi-square test theorem to redefine the cost function and the error function. The weights and thresholds are modified using standard backpropagation algorithm. The proposed approach has the advantage of making consistent data distribution over training and testing sets. It can be used for binary classification. The experimental results on real world data sets indicate that the proposed algorithm can significantly improve the classification accuracy comparing to related approaches. |
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| Published | 2018-09-04 |
| URL | http://arxiv.org/abs/1809.01079v1 |
| http://arxiv.org/pdf/1809.01079v1.pdf | |
| PWC | https://paperswithcode.com/paper/chi-square-test-neural-network-a-new-binary |
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Comparison between DeepESNs and gated RNNs on multivariate time-series prediction
| Title | Comparison between DeepESNs and gated RNNs on multivariate time-series prediction |
| Authors | Claudio Gallicchio, Alessio Micheli, Luca Pedrelli |
| Abstract | We propose an experimental comparison between Deep Echo State Networks (DeepESNs) and gated Recurrent Neural Networks (RNNs) on multivariate time-series prediction tasks. In particular, we compare reservoir and fully-trained RNNs able to represent signals featured by multiple time-scales dynamics. The analysis is performed in terms of efficiency and prediction accuracy on 4 polyphonic music tasks. Our results show that DeepESN is able to outperform ESN in terms of prediction accuracy and efficiency. Whereas, between fully-trained approaches, Gated Recurrent Units (GRU) outperforms Long Short-Term Memory (LSTM) and simple RNN models in most cases. Overall, DeepESN turned out to be extremely more efficient than others RNN approaches and the best solution in terms of prediction accuracy on 3 out of 4 tasks. |
| Tasks | Time Series, Time Series Prediction |
| Published | 2018-12-30 |
| URL | https://arxiv.org/abs/1812.11527v2 |
| https://arxiv.org/pdf/1812.11527v2.pdf | |
| PWC | https://paperswithcode.com/paper/comparison-between-deepesns-and-gated-rnns-on |
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Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
| Title | Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 |
| Authors | Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi |
| Abstract | This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada. |
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| Published | 2018-11-17 |
| URL | http://arxiv.org/abs/1811.07216v2 |
| http://arxiv.org/pdf/1811.07216v2.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-for-health-ml4h-workshop-at |
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Exploiting Explicit Paths for Multi-hop Reading Comprehension
| Title | Exploiting Explicit Paths for Multi-hop Reading Comprehension |
| Authors | Souvik Kundu, Tushar Khot, Ashish Sabharwal, Peter Clark |
| Abstract | We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching state-of-the-art performance. |
| Tasks | Knowledge Graphs, Multi-Hop Reading Comprehension, Reading Comprehension |
| Published | 2018-11-02 |
| URL | https://arxiv.org/abs/1811.01127v2 |
| https://arxiv.org/pdf/1811.01127v2.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-explicit-paths-for-multi-hop |
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Color naming reflects both perceptual structure and communicative need
| Title | Color naming reflects both perceptual structure and communicative need |
| Authors | Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier |
| Abstract | Gibson et al. (2017) argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support more precise communication about warm colors than cool colors, and that the objects we talk about tend to be warm-colored rather than cool-colored. Here, we present new analyses that alter this picture. We show that greater communicative precision for warm than for cool colors, and greater communicative need, may both be explained by perceptual structure. However, using an information-theoretic analysis, we also show that color naming across languages bears signs of communicative need beyond what would be predicted by perceptual structure alone. We conclude that color naming is shaped both by perceptual structure, as has traditionally been argued, and by patterns of communicative need, as argued by Gibson et al. - although for reasons other than those they advanced. |
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| Published | 2018-05-16 |
| URL | http://arxiv.org/abs/1805.06165v3 |
| http://arxiv.org/pdf/1805.06165v3.pdf | |
| PWC | https://paperswithcode.com/paper/color-naming-reflects-both-perceptual |
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CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
| Title | CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction |
| Authors | Murari Mandal, Prafulla Saxena, Santosh Kumar Vipparthi, Subrahmanyam Murala |
| Abstract | Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches. |
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| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07008v1 |
| http://arxiv.org/pdf/1804.07008v1.pdf | |
| PWC | https://paperswithcode.com/paper/candid-robust-change-dynamics-and |
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Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning
| Title | Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning |
| Authors | Xiao Wang, Tao Sun, Rui Yang, Chenglong Li, Bin Luo, Jin Tang |
| Abstract | Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL). Specifically, we take the weighting of each domain as a decision-making problem and teach an agent learn to interact with the environment. The agent can tune the weight of each domain through discrete action selection and obtain a positive reward if the saliency results are improved. The target of the agent is to achieve maximum rewards after finished its sequential action selection. We validate the proposed algorithms on multimodal saliency detection in a coarse-to-fine way. The coarse saliency maps are generated from an encoder-decoder framework which is trained with content loss and adversarial loss. The final results can be obtained via adaptive weighting of maps from each domain. Experiments conducted on two kinds of salient object detection benchmarks validated the effectiveness of our proposed quality-aware deep neural network. |
| Tasks | Decision Making, Object Detection, Saliency Detection, Salient Object Detection |
| Published | 2018-11-27 |
| URL | http://arxiv.org/abs/1811.10763v1 |
| http://arxiv.org/pdf/1811.10763v1.pdf | |
| PWC | https://paperswithcode.com/paper/quality-aware-multimodal-saliency-detection |
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NSEEN: Neural Semantic Embedding for Entity Normalization
| Title | NSEEN: Neural Semantic Embedding for Entity Normalization |
| Authors | Shobeir Fakhraei, Joel Mathew, Jose Luis Ambite |
| Abstract | Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets. |
| Tasks | Entity Resolution, Knowledge Graphs |
| Published | 2018-11-19 |
| URL | https://arxiv.org/abs/1811.07514v2 |
| https://arxiv.org/pdf/1811.07514v2.pdf | |
| PWC | https://paperswithcode.com/paper/nseen-neural-semantic-embedding-for-entity |
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Data-driven Air Quality Characterisation for Urban Environments: a Case Study
| Title | Data-driven Air Quality Characterisation for Urban Environments: a Case Study |
| Authors | Yuchao Zhou, Suparna De, Gideon Ewa, Charith Perera, Klaus Moessner |
| Abstract | The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region. |
| Tasks | Time Series, Time Series Prediction |
| Published | 2018-12-01 |
| URL | http://arxiv.org/abs/1901.06242v1 |
| http://arxiv.org/pdf/1901.06242v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-air-quality-characterisation-for |
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Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection
| Title | Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection |
| Authors | Ziqi Zhou, Zheng Wang, Huchuan Lu, Song Wang, Meijun Sun |
| Abstract | The existing still-static deep learning based saliency researches do not consider the weighting and highlighting of extracted features from different layers, all features contribute equally to the final saliency decision-making. Such methods always evenly detect all “potentially significant regions” and unable to highlight the key salient object, resulting in detection failure of dynamic scenes. In this paper, based on the fact that salient areas in videos are relatively small and concentrated, we propose a \textbf{key salient object re-augmentation method (KSORA) using top-down semantic knowledge and bottom-up feature guidance} to improve detection accuracy in video scenes. KSORA includes two sub-modules (WFE and KOS): WFE processes local salient feature selection using bottom-up strategy, while KOS ranks each object in global fashion by top-down statistical knowledge, and chooses the most critical object area for local enhancement. The proposed KSORA can not only strengthen the saliency value of the local key salient object but also ensure global saliency consistency. Results on three benchmark datasets suggest that our model has the capability of improving the detection accuracy on complex scenes. The significant performance of KSORA, with a speed of 17FPS on modern GPUs, has been verified by comparisons with other ten state-of-the-art algorithms. |
| Tasks | Decision Making, Feature Selection, Saliency Detection, Video Saliency Detection |
| Published | 2018-11-19 |
| URL | http://arxiv.org/abs/1811.07480v1 |
| http://arxiv.org/pdf/1811.07480v1.pdf | |
| PWC | https://paperswithcode.com/paper/global-and-local-sensitivity-guided-key |
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HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images
| Title | HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images |
| Authors | Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Nam Ling |
| Abstract | Co-saliency detection aims to discover common and salient objects in an image group containing more than two relevant images. Moreover, depth information has been demonstrated to be effective for many computer vision tasks. In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement. With the assistance of the intra saliency map, the inter-image correspondence is formulated as a hierarchical sparsity reconstruction framework. The global sparsity reconstruction model with a ranking scheme focuses on capturing the global characteristics among the whole image group through a common foreground dictionary. The pairwise sparsity reconstruction model aims to explore the corresponding relationship between pairwise images through a set of pairwise dictionaries. In order to improve the intra-image smoothness and inter-image consistency, an energy function refinement model is proposed, which includes the unary data term, spatial smooth term, and holistic consistency term. Experiments on two RGBD co-saliency detection benchmarks demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively. |
| Tasks | Co-Saliency Detection, Saliency Detection |
| Published | 2018-11-16 |
| URL | http://arxiv.org/abs/1811.06679v1 |
| http://arxiv.org/pdf/1811.06679v1.pdf | |
| PWC | https://paperswithcode.com/paper/hscs-hierarchical-sparsity-based-co-saliency |
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Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
| Title | Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features |
| Authors | Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Mubarak Shah |
| Abstract | This paper tackles the problem of learning brain-visual representations for understanding and neural processes behind human visual perception, with a view towards replicating these processes into machines. The core idea is to learn plausible representations through the combined use of human neural activity evoked by natural images as a supervision mechanism for deep learning models. To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation. The learned manifold is then used to perform image classification and saliency detection as well as to shed light on the possible representations generated by the human brain when perceiving the visual world. Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection. Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and, most importantly, highlights new associations between brain areas, image patches and computational kernels. In particular, we are able to approximate brain responses to visual stimuli by training an artificial model with image features correlated to neural activity. |
| Tasks | Image Classification, Saliency Detection |
| Published | 2018-10-25 |
| URL | http://arxiv.org/abs/1810.10974v1 |
| http://arxiv.org/pdf/1810.10974v1.pdf | |
| PWC | https://paperswithcode.com/paper/decoding-brain-representations-by-multimodal |
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Salient Object Detection in Video using Deep Non-Local Neural Networks
| Title | Salient Object Detection in Video using Deep Non-Local Neural Networks |
| Authors | Mohammad Shokri, Ahad Harati, Kimya Taba |
| Abstract | Detection of salient objects in image and video is of great importance in many computer vision applications. In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection. This paper investigates the use of recently introduced non-local neural networks in video salient object detection. Non-local neural networks are applied to capture global dependencies and hence determine the salient objects. The effect of non-local operations is studied separately on static and dynamic saliency detection in order to exploit both appearance and motion features. A novel deep non-local neural network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods. |
| Tasks | Object Detection, Saliency Detection, Salient Object Detection, Video Saliency Detection, Video Salient Object Detection |
| Published | 2018-10-16 |
| URL | http://arxiv.org/abs/1810.07097v1 |
| http://arxiv.org/pdf/1810.07097v1.pdf | |
| PWC | https://paperswithcode.com/paper/salient-object-detection-in-video-using-deep |
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SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection
| Title | SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection |
| Authors | Meijun Sun, Ziqi Zhou, QinGhua Hu, Zheng Wang, Jianmin Jiang |
| Abstract | Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of imagebased salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets. |
| Tasks | Saliency Detection, Video Saliency Detection |
| Published | 2018-09-21 |
| URL | http://arxiv.org/abs/1809.07988v1 |
| http://arxiv.org/pdf/1809.07988v1.pdf | |
| PWC | https://paperswithcode.com/paper/sg-fcn-a-motion-and-memory-based-deep |
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Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition
| Title | Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition |
| Authors | Rahma Fourati, Boudour Ammar, Javier Sanchez-Medina, Adel M. Alimi |
| Abstract | In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. Actually, the neural plasticity rules are a kind of unsupervised learning adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an investigation of Oja’s rule, BCM rule and gaussian intrinsic plasticity rule was carried out in the context of EEG-based emotion recognition. The study, also, includes a comparison of the offline and online training of the ESN. When testing on the well-known affective benchmark “DEAP dataset” which contains EEG signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic plasticity enhanced the classification accuracy and outperformed the results achieved with an ESN pretrained with synaptic plasticity. Four classification problems were conducted in which the system complexity is increased and the discrimination is more challenging, i.e. inter-subject emotion discrimination. Our proposed method achieves higher performance over the state of the art methods. |
| Tasks | EEG, Emotion Recognition, Time Series, Time Series Prediction |
| Published | 2018-11-19 |
| URL | http://arxiv.org/abs/1811.07516v2 |
| http://arxiv.org/pdf/1811.07516v2.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-learning-in-reservoir-computing |
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