Paper Group ANR 692
Talking with Robots: Opportunities and Challenges. Non-parametric Probabilistic Load Flow using Gaussian Process Learning. RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation. SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System. Bounded Residual Gradient Networks (BReG-Net) for Facial A …
Talking with Robots: Opportunities and Challenges
| Title | Talking with Robots: Opportunities and Challenges |
| Authors | Roger K. Moore |
| Abstract | Notwithstanding the tremendous progress that is taking place in spoken language technology, effective speech-based human-robot interaction still raises a number of important challenges. Not only do the fields of robotics and spoken language technology present their own special problems, but their combination raises an additional set of issues. In particular, there is a large gap between the formulaic speech that typifies contemporary spoken dialogue systems and the flexible nature of human-human conversation. It is pointed out that grounded and situated speech-based human-robot interaction may lead to deeper insights into the pragmatics of language usage, thereby overcoming the current `habitability gap’. | |
| Tasks | Spoken Dialogue Systems |
| Published | 2019-12-01 |
| URL | https://arxiv.org/abs/1912.00369v1 |
| https://arxiv.org/pdf/1912.00369v1.pdf | |
| PWC | https://paperswithcode.com/paper/talking-with-robots-opportunities-and |
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Non-parametric Probabilistic Load Flow using Gaussian Process Learning
| Title | Non-parametric Probabilistic Load Flow using Gaussian Process Learning |
| Authors | Parikshit Pareek, Chuan Wang, Hung D. Nguyen |
| Abstract | In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncertainty for better operational decisions. The technique can provide “semi-explicit” power flow solutions by implementing the learning and testing steps which map control variables to inputs. The proposed NP-PLF leverages upon GP upper confidence bound (GP-UCB) sampling algorithm. The salient features of this NP-PLF method are: i) applicable for power flow problem having power injection uncertainty with an unknown class of distribution; ii) providing probabilistic learning bound (PLB) which further provides control over the error and convergence; iii) capable of handling intermittent distributed generation as well as load uncertainties, and iv) applicable to both balanced and unbalanced power flow with different type and size of power systems. The simulation results performed on the IEEE 30-bus and IEEE 118-bus system show that the proposed method can learn the voltage function over the power injection subspace using a small number of training samples. Further, the testing with different input uncertainty distributions indicates that complete statistical information can be obtained for the probabilistic load flow problem with average percentage relative error of order $10^{-3}$% on 50000 test points. |
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| Published | 2019-11-08 |
| URL | https://arxiv.org/abs/1911.03093v2 |
| https://arxiv.org/pdf/1911.03093v2.pdf | |
| PWC | https://paperswithcode.com/paper/non-parametric-probabilistic-load-flow-using |
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RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation
| Title | RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation |
| Authors | Xuesong Niu, Shiguang Shan, Hu Han, Xilin Chen |
| Abstract | Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos; however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HR, available at ‘http://vipl.ict.ac.cn/view_database.php?id=15'), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases. |
| Tasks | Heart rate estimation |
| Published | 2019-10-25 |
| URL | https://arxiv.org/abs/1910.11515v2 |
| https://arxiv.org/pdf/1910.11515v2.pdf | |
| PWC | https://paperswithcode.com/paper/rhythmnet-end-to-end-heart-rate-estimation |
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SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
| Title | SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System |
| Authors | Khalil Damak, Olfa Nasraoui |
| Abstract | State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning model, called “SeER”, that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations; solve the item cold start problem; and generate a relevant explanation for a song recommendation. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation. Moreover, based on proposed tests for offline validation, we show that our personalized explanations capture properties that are in accordance with the user’s preferences. |
| Tasks | Recommendation Systems |
| Published | 2019-06-25 |
| URL | https://arxiv.org/abs/1907.01640v2 |
| https://arxiv.org/pdf/1907.01640v2.pdf | |
| PWC | https://paperswithcode.com/paper/seer-an-explainable-deep-learning-midi-based |
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Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing
| Title | Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing |
| Authors | Behzad Hasani, Pooran Singh Negi, Mohammad H. Mahoor |
| Abstract | Residual-based neural networks have shown remarkable results in various visual recognition tasks including Facial Expression Recognition (FER). Despite the tremendous efforts have been made to improve the performance of FER systems using DNNs, existing methods are not generalizable enough for practical applications. This paper introduces Bounded Residual Gradient Networks (BReG-Net) for facial expression recognition, in which the shortcut connection between the input and the output of the ResNet module is replaced with a differentiable function with a bounded gradient. This configuration prevents the network from facing the vanishing or exploding gradient problem. We show that utilizing such non-linear units will result in shallower networks with better performance. Further, by using a weighted loss function which gives a higher priority to less represented categories, we can achieve an overall better recognition rate. The results of our experiments show that BReG-Nets outperform state-of-the-art methods on three publicly available facial databases in the wild, on both the categorical and dimensional models of affect. |
| Tasks | Facial Expression Recognition |
| Published | 2019-03-05 |
| URL | http://arxiv.org/abs/1903.02110v1 |
| http://arxiv.org/pdf/1903.02110v1.pdf | |
| PWC | https://paperswithcode.com/paper/bounded-residual-gradient-networks-breg-net |
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CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
| Title | CoSimLex: A Resource for Evaluating Graded Word Similarity in Context |
| Authors | Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Marko Robnik-Šikonja, Mark Granroth-Wilding, Kristiina Vaik |
| Abstract | State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far. |
| Tasks | Word Embeddings, Word Sense Disambiguation |
| Published | 2019-12-11 |
| URL | https://arxiv.org/abs/1912.05320v2 |
| https://arxiv.org/pdf/1912.05320v2.pdf | |
| PWC | https://paperswithcode.com/paper/cosimlex-a-resource-for-evaluating-graded |
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Active Stacking for Heart Rate Estimation
| Title | Active Stacking for Heart Rate Estimation |
| Authors | Dongrui Wu, Feifei Liu, Chengyu Liu |
| Abstract | Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorithm may break down on certain subjects, resulting in a significant estimation error. Ensemble regression, which aggregates the outputs of multiple base estimators for more reliable and stable estimates, can be used to remedy this problem. Moreover, active learning can be used to optimally select a few trials from a new subject to label, based on which a stacking ensemble regression model can be trained to aggregate the base estimators. This paper proposes four active stacking approaches, and demonstrates that they all significantly outperform three common unsupervised ensemble regression approaches, and a supervised stacking approach which randomly selects some trials to label. Remarkably, our active stacking approaches only need three or four labeled trials from each subject to achieve an average root mean squared estimation error below three beats per minute, making them very convenient for real-world applications. To our knowledge, this is the first research on active stacking, and its application to heart rate estimation. |
| Tasks | Active Learning, Heart rate estimation |
| Published | 2019-03-26 |
| URL | http://arxiv.org/abs/1903.10862v1 |
| http://arxiv.org/pdf/1903.10862v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-stacking-for-heart-rate-estimation |
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Active Learning for Graph Neural Networks via Node Feature Propagation
| Title | Active Learning for Graph Neural Networks via Node Feature Propagation |
| Authors | Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski |
| Abstract | Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly. |
| Tasks | Active Learning, Node Classification |
| Published | 2019-10-16 |
| URL | https://arxiv.org/abs/1910.07567v1 |
| https://arxiv.org/pdf/1910.07567v1.pdf | |
| PWC | https://paperswithcode.com/paper/active-learning-for-graph-neural-networks-via |
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Detection of False Positive and False Negative Samples in Semantic Segmentation
| Title | Detection of False Positive and False Negative Samples in Semantic Segmentation |
| Authors | Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk |
| Abstract | In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions. |
| Tasks | Autonomous Driving, Semantic Segmentation |
| Published | 2019-12-08 |
| URL | https://arxiv.org/abs/1912.03673v1 |
| https://arxiv.org/pdf/1912.03673v1.pdf | |
| PWC | https://paperswithcode.com/paper/detection-of-false-positive-and-false |
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A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
| Title | A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down? |
| Authors | Mario Bijelic, Tobias Gruber, Werner Ritter |
| Abstract | Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data. |
| Tasks | Autonomous Driving |
| Published | 2019-12-06 |
| URL | https://arxiv.org/abs/1912.03251v1 |
| https://arxiv.org/pdf/1912.03251v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-benchmark-for-lidar-sensors-in-fog-is |
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Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
| Title | Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving |
| Authors | Mario Bijelic, Tobias Gruber, Werner Ritter |
| Abstract | Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination. |
| Tasks | Autonomous Driving |
| Published | 2019-12-06 |
| URL | https://arxiv.org/abs/1912.03238v1 |
| https://arxiv.org/pdf/1912.03238v1.pdf | |
| PWC | https://paperswithcode.com/paper/benchmarking-image-sensors-under-adverse |
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HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation
| Title | HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation |
| Authors | Kun Zhou, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu |
| Abstract | Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state - Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps to represent the relative depth information of the end-joints for each skeletal body part. In our approach, a Convolutional Network (ConvNet) is first trained to predict HEMlests from the input image, followed by a volumetric joint-heatmap regression. We leverage on the integral operation to extract the joint locations from the volumetric heatmaps, guaranteeing end-to-end learning. Despite the simplicity of the network design, the quantitative comparisons show a significant performance improvement over the best-of-grade method (by 20% on Human3.6M). The proposed method naturally supports training with “in-the-wild” images, where only weakly-annotated relative depth information of skeletal joints is available. This further improves the generalization ability of our model, as validated by qualitative comparisons on outdoor images. |
| Tasks | 3D Human Pose Estimation, Pose Estimation |
| Published | 2019-10-26 |
| URL | https://arxiv.org/abs/1910.12032v1 |
| https://arxiv.org/pdf/1910.12032v1.pdf | |
| PWC | https://paperswithcode.com/paper/hemlets-pose-learning-part-centric-heatmap-1 |
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When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
| Title | When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies |
| Authors | Zheyu Yan, Yiyu Shi, Wang Liao, Masanori Hashimoto, Xichuan Zhou, Cheng Zhuo |
| Abstract | Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems. However, security-sensitive systems demand not only high perception performance, but also design robustness under various circumstances. Unlike prior works that study network robustness from software level, we investigate from hardware perspective about the impact of Single Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We systematically define the fault models of SEU and then provide the definition of sensitivity to SIPP as the robustness measure for the network. We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth. Based on those findings, we propose two remedy solutions to protect DNNs from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for ResNet with merely 0.24-bit SRAM area overhead per parameter. |
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| Published | 2019-09-10 |
| URL | https://arxiv.org/abs/1909.04697v1 |
| https://arxiv.org/pdf/1909.04697v1.pdf | |
| PWC | https://paperswithcode.com/paper/when-single-event-upset-meets-deep-neural |
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PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram
| Title | PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram |
| Authors | Shyam A, Vignesh Ravichandran, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam |
| Abstract | Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in free living conditions. PPG based heart rate measurement is unfortunately highly susceptible to motion artifacts, particularly when measured from the wrist. Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation. The conventional learning based approaches have not addressed the need for device-specific modeling due to differences in hardware design among PPG devices. In this paper, we propose a novel end to end deep learning model to perform heart rate estimation using 8 second length input PPG signal. We evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean absolute error of 3.36+-4.1BPM for HR estimation on 12 subjects without requiring patient specific training. We also studied the feasibility of applying transfer learning along with sparse retraining from a comprehensive in house PPG dataset for heart rate estimation across PPG devices with different hardware design. |
| Tasks | Heart rate estimation, Transfer Learning |
| Published | 2019-03-21 |
| URL | http://arxiv.org/abs/1903.08912v1 |
| http://arxiv.org/pdf/1903.08912v1.pdf | |
| PWC | https://paperswithcode.com/paper/ppgnet-deep-network-for-device-independent |
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Magnitude and Uncertainty Pruning Criterion for Neural Networks
| Title | Magnitude and Uncertainty Pruning Criterion for Neural Networks |
| Authors | Vinnie Ko, Stefan Oehmcke, Fabian Gieseke |
| Abstract | Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many of these models are overparameterized, which makes them both computationally and memory intensive. Furthermore, overparameterization can also lead to undesired overfitting side-effects. Inspired by recently proposed magnitude-based pruning schemes and the Wald test from the field of statistics, we introduce a novel magnitude and uncertainty (M&U) pruning criterion that helps to lessen such shortcomings. One important advantage of our M&U pruning criterion is that it is scale-invariant, a phenomenon that the magnitude-based pruning criterion suffers from. In addition, we present a ``pseudo bootstrap’’ scheme, which can efficiently estimate the uncertainty of the weights by using their update information during training. Our experimental evaluation, which is based on various neural network architectures and datasets, shows that our new criterion leads to more compressed models compared to models that are solely based on magnitude-based pruning criteria, with, at the same time, less loss in predictive power. | |
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| Published | 2019-12-10 |
| URL | https://arxiv.org/abs/1912.04845v1 |
| https://arxiv.org/pdf/1912.04845v1.pdf | |
| PWC | https://paperswithcode.com/paper/magnitude-and-uncertainty-pruning-criterion |
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