Paper Group ANR 1634
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting. Data-driven Policy on Feasibility Determination for the Train Shunting Problem. SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules. U-CNNpred: A Universal CNN-based Predictor for Stock Markets. An Algorithmic In …
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting
| Title | A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting |
| Authors | Pei Du, Jianzhou Wang, Yan Hao, Tong Niu, Wendong Yang |
| Abstract | High levels of air pollution may seriously affect people’s living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, most previous research have many deficiencies, such as ignoring the importance of predictive stability, and poor initial parameters and so on, which have significantly effect on the performance of air pollution prediction. Therefore, to address these issues, a novel hybrid model is proposed in this study. Specifically, a powerful data preprocessing techniques is applied to decompose the original time series into different modes from low- frequency to high- frequency. Next, a new multi-objective algorithm called MOHHO is first developed in this study, which are introduced to tune the parameters of ELM model with high forecasting accuracy and stability for air pollution series prediction, simultaneously. And the optimized ELM model is used to perform the time series prediction. Finally, a scientific and robust evaluation system including several error criteria, benchmark models, and several experiments using six air pollutant concentrations time series from three cities in China is designed to perform a compressive assessment for the presented hybrid forecasting model. Experimental results indicate that the proposed hybrid model can guarantee a more stable and higher predictive performance compared to others, whose superior prediction ability may help to develop effective plans for air pollutant emissions and prevent health problems caused by air pollution. |
| Tasks | Air Pollution Prediction, Time Series, Time Series Prediction |
| Published | 2019-05-30 |
| URL | https://arxiv.org/abs/1905.13550v1 |
| https://arxiv.org/pdf/1905.13550v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-novel-hybrid-model-based-on-multi-objective |
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Data-driven Policy on Feasibility Determination for the Train Shunting Problem
| Title | Data-driven Policy on Feasibility Determination for the Train Shunting Problem |
| Authors | Paulo R. de O. da Costa, J. Rhuggenaath, Y. Zhang, A. Akcay, W. Lee, U. Kaymak |
| Abstract | Parking, matching, scheduling, and routing are common problems in train maintenance. In particular, train units are commonly maintained and cleaned at dedicated shunting yards. The planning problem that results from such situations is referred to as the Train Unit Shunting Problem (TUSP). This problem involves matching arriving train units to service tasks and determining the schedule for departing trains. The TUSP is an important problem as it is used to determine the capacity of shunting yards and arises as a sub-problem of more general scheduling and planning problems. In this paper, we consider the case of the Dutch Railways (NS) TUSP. As the TUSP is complex, NS currently uses a local search (LS) heuristic to determine if an instance of the TUSP has a feasible solution. Given the number of shunting yards and the size of the planning problems, improving the evaluation speed of the LS brings significant computational gain. In this work, we use a machine learning approach that complements the LS and accelerates the search process. We use a Deep Graph Convolutional Neural Network (DGCNN) model to predict the feasibility of solutions obtained during the run of the LS heuristic. We use this model to decide whether to continue or abort the search process. In this way, the computation time is used more efficiently as it is spent on instances that are more likely to be feasible. Using simulations based on real-life instances of the TUSP, we show how our approach improves upon the previous method on prediction accuracy and leads to computational gains for the decision-making process. |
| Tasks | Decision Making |
| Published | 2019-07-10 |
| URL | https://arxiv.org/abs/1907.04711v1 |
| https://arxiv.org/pdf/1907.04711v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-policy-on-feasibility |
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SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
| Title | SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules |
| Authors | Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun |
| Abstract | Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient’s polysomnogram (PSG) from a single night. Although deep learning models have demonstrated state-of-the-art performance in automating sleep staging, interpretability which defines other desiderata, has largely remained unexplored. In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models. In particular, SLEEPER utilizes sleep scoring rules and expert defined features to derive prototypes which are embeddings of PSG data fragments via convolutional neural networks. The final models are simple interpretable models like a shallow decision tree defined over those phenotypes. We evaluated SLEEPER using two PSG datasets collected from sleep studies and demonstrated that SLEEPER could provide accurate sleep stage classification comparable to human experts and deep neural networks with about 85% ROC-AUC and .7 kappa. |
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| Published | 2019-10-14 |
| URL | https://arxiv.org/abs/1910.06100v1 |
| https://arxiv.org/pdf/1910.06100v1.pdf | |
| PWC | https://paperswithcode.com/paper/sleeper-interpretable-sleep-staging-via |
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U-CNNpred: A Universal CNN-based Predictor for Stock Markets
| Title | U-CNNpred: A Universal CNN-based Predictor for Stock Markets |
| Authors | Ehsan Hoseinzade, Saman Haratizadeh, Arash Khoeini |
| Abstract | The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch. |
| Tasks | Stock Market Prediction |
| Published | 2019-11-28 |
| URL | https://arxiv.org/abs/1911.12540v1 |
| https://arxiv.org/pdf/1911.12540v1.pdf | |
| PWC | https://paperswithcode.com/paper/u-cnnpred-a-universal-cnn-based-predictor-for |
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An Algorithmic Inference Approach to Learn Copulas
| Title | An Algorithmic Inference Approach to Learn Copulas |
| Authors | Bruno Apolloni |
| Abstract | We introduce a new method for estimating the parameter of the bivariate Clayton copulas within the framework of Algorithmic Inference. The method consists of a variant of the standard boot-strapping procedure for inferring random parameters, which we expressly devise to bypass the two pitfalls of this specific instance: the non independence of the Kendall statistics, customarily at the basis of this inference task, and the absence of a sufficient statistic w.r.t. \alpha. The variant is rooted on a numerical procedure in order to find the \alpha estimate at a fixed point of an iterative routine. Although paired with the customary complexity of the program which computes them, numerical results show an outperforming accuracy of the estimates. |
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| Published | 2019-10-07 |
| URL | https://arxiv.org/abs/1910.02678v1 |
| https://arxiv.org/pdf/1910.02678v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-algorithmic-inference-approach-to-learn |
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Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
| Title | Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning |
| Authors | Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor |
| Abstract | Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency. In this paper, we contribute a novel self-supervised auxiliary task, i.e., Terminal Prediction (TP), estimating temporal closeness to terminal states for episodic tasks. The intuition is to help representation learning by letting the agent predict how close it is to a terminal state, while learning its control policy. Although TP could be integrated with multiple algorithms, this paper focuses on Asynchronous Advantage Actor-Critic (A3C) and demonstrating the advantages of A3C-TP. Our extensive evaluation includes: a set of Atari games, the BipedalWalker domain, and a mini version of the recently proposed multi-agent Pommerman game. Our results on Atari games and the BipedalWalker domain suggest that A3C-TP outperforms standard A3C in most of the tested domains and in others it has similar performance. In Pommerman, our proposed method provides significant improvement both in learning efficiency and converging to better policies against different opponents. |
| Tasks | Atari Games, Representation Learning |
| Published | 2019-07-24 |
| URL | https://arxiv.org/abs/1907.10827v1 |
| https://arxiv.org/pdf/1907.10827v1.pdf | |
| PWC | https://paperswithcode.com/paper/terminal-prediction-as-an-auxiliary-task-for |
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Hindsight Trust Region Policy Optimization
| Title | Hindsight Trust Region Policy Optimization |
| Authors | Hanbo Zhang, Site Bai, Xuguang Lan, David Hsu, Nanning Zheng |
| Abstract | Reinforcement Learning(RL) with sparse rewards is a major challenge. We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards. Hindsight refers to the algorithm’s ability to learn from information across goals, including ones not intended for the current task. HTRPO leverages two main ideas. It introduces QKL, a quadratic approximation to the KL divergence constraint on the trust region, leading to reduced variance in KL divergence estimation and improved stability in policy update. It also presents Hindsight Goal Filtering(HGF) to select conductive hindsight goals. In experiments, we evaluate HTRPO in various sparse reward tasks, including simple benchmarks, image-based Atari games, and simulated robot control. Ablation studies indicate that QKL and HGF contribute greatly to learning stability and high performance. Comparison results show that in all tasks, HTRPO consistently outperforms both TRPO and HPG, a state-of-the-art algorithm for RL with sparse rewards. |
| Tasks | Atari Games, Policy Gradient Methods |
| Published | 2019-07-29 |
| URL | https://arxiv.org/abs/1907.12439v3 |
| https://arxiv.org/pdf/1907.12439v3.pdf | |
| PWC | https://paperswithcode.com/paper/hindsight-trust-region-policy-optimization |
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Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling
| Title | Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling |
| Authors | Yuan-Hao Yi, Yang Ai, Zhen-Hua Ling, Li-Rong Dai |
| Abstract | This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains more local dynamic movements of acoustic features, e.g., vibratos. Therefore, our method adopts deep autoregressive (DAR) models to predict the F0 and spectral features of singing voice in order to better describe the dependencies among the acoustic features of consecutive frames. For F0 modeling, discretized F0 values are used and the influences of the history length in DAR are analyzed by experiments. An F0 post-processing strategy is also designed to alleviate the inconsistency between the predicted F0 contours and the F0 values determined by music notes. Furthermore, we extend the DAR model to deal with continuous spectral features, and a prenet module with self-attention layers is introduced to process historical frames. Experiments on a Chinese singing voice corpus demonstrate that our method using DARs can produce F0 contours with vibratos effectively, and can achieve better objective and subjective performance than the conventional method using recurrent neural networks (RNNs). |
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| Published | 2019-06-21 |
| URL | https://arxiv.org/abs/1906.08977v1 |
| https://arxiv.org/pdf/1906.08977v1.pdf | |
| PWC | https://paperswithcode.com/paper/singing-voice-synthesis-using-deep |
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Proximal Policy Optimization with Mixed Distributed Training
| Title | Proximal Policy Optimization with Mixed Distributed Training |
| Authors | Zhenyu Zhang, Xiangfeng Luo, Tong Liu, Shaorong Xie, Jianshu Wang, Wei Wang, Yang Li, Yan Peng |
| Abstract | Instability and slowness are two main problems in deep reinforcement learning. Even if proximal policy optimization (PPO) is the state of the art, it still suffers from these two problems. We introduce an improved algorithm based on proximal policy optimization, mixed distributed proximal policy optimization (MDPPO), and show that it can accelerate and stabilize the training process. In our algorithm, multiple different policies train simultaneously and each of them controls several identical agents that interact with environments. Actions are sampled by each policy separately as usual, but the trajectories for the training process are collected from all agents, instead of only one policy. We find that if we choose some auxiliary trajectories elaborately to train policies, the algorithm will be more stable and quicker to converge especially in the environments with sparse rewards. |
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| Published | 2019-07-15 |
| URL | https://arxiv.org/abs/1907.06479v3 |
| https://arxiv.org/pdf/1907.06479v3.pdf | |
| PWC | https://paperswithcode.com/paper/proximal-policy-optimization-with-mixed |
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Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design
| Title | Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design |
| Authors | Alexander Gee, Hussein Abbass |
| Abstract | Swarm control is a difficult problem due to the need to guide a large number of agents simultaneously. We cast the problem as a shepherding problem, similar to biological dogs guiding a group of sheep towards a goal. The shepherd needs to deal with complex and dynamic environments and make decisions in order to direct the swarm from one location to another. In this paper, we design a novel curriculum to teach an artificial intelligence empowered agent to shepherd in the presence of the large state space associated with the shepherding problem and in a transparent manner. The results show that a properly designed curriculum could indeed enhance the speed of learning and the complexity of learnt behaviours. |
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| Published | 2019-01-04 |
| URL | http://arxiv.org/abs/1903.09297v1 |
| http://arxiv.org/pdf/1903.09297v1.pdf | |
| PWC | https://paperswithcode.com/paper/transparent-machine-education-of-neural |
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Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa
| Title | Prevalence of code mixing in semi-formal patient communication in low resource languages of South Africa |
| Authors | Monika Obrocka, Charles Copley, Themba Gqaza, Eli Grant |
| Abstract | In this paper we address the problem of code-mixing in resource-poor language settings. We examine data consisting of 182k unique questions generated by users of the MomConnect helpdesk, part of a national scale public health platform in South Africa. We show evidence of code-switching at the level of approximately 10% within this dataset – a level that is likely to pose challenges for future services. We use a natural language processing library (Polyglot) that supports detection of 196 languages and attempt to evaluate its performance at identifying English, isiZulu and code-mixed questions. |
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| Published | 2019-11-13 |
| URL | https://arxiv.org/abs/1911.05636v3 |
| https://arxiv.org/pdf/1911.05636v3.pdf | |
| PWC | https://paperswithcode.com/paper/prevalence-of-code-mixing-in-semi-formal |
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Reachable Space Characterization of Markov Decision Processes with Time Variability
| Title | Reachable Space Characterization of Markov Decision Processes with Time Variability |
| Authors | Junhong Xu, Kai Yin, Lantao Liu |
| Abstract | We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states’ reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time. |
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| Published | 2019-05-22 |
| URL | https://arxiv.org/abs/1905.09342v2 |
| https://arxiv.org/pdf/1905.09342v2.pdf | |
| PWC | https://paperswithcode.com/paper/reachable-space-characterization-of-markov |
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GetNet: Get Target Area for Image Pairing
| Title | GetNet: Get Target Area for Image Pairing |
| Authors | Henry H. Yu, Jiang Liu, Hao Sun, Ziwen Wang, Haotian Zhang |
| Abstract | Image pairing is an important research task in the field of computer vision. And finding image pairs containing objects of the same category is the basis of many tasks such as tracking and person re-identification, etc., and it is also the focus of our research. Existing traditional methods and deep learning-based methods have some degree of defects in speed or accuracy. In this paper, we made improvements on the Siamese network and proposed GetNet. The proposed method GetNet combines STN and Siamese network to get the target area first and then perform subsequent processing. Experiments show that our method achieves competitive results in speed and accuracy. |
| Tasks | Person Re-Identification |
| Published | 2019-10-08 |
| URL | https://arxiv.org/abs/1910.03152v1 |
| https://arxiv.org/pdf/1910.03152v1.pdf | |
| PWC | https://paperswithcode.com/paper/getnet-get-target-area-for-image-pairing |
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Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
| Title | Multimodal Intelligence: Representation Learning, Information Fusion, and Applications |
| Authors | Chao Zhang, Zichao Yang, Xiaodong He, Li Deng |
| Abstract | Deep learning has revolutionized speech recognition, image recognition, and natural language processing since 2010, each involving a single modality in the input signal. However, many applications in artificial intelligence involve more than one modality. It is therefore of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, a technical review of the models and learning methods for multimodal intelligence is provided. The main focus is the combination of vision and natural language, which has become an important area in both computer vision and natural language processing research communities. This review provides a comprehensive analysis of recent work on multimodal deep learning from three angles — learning multimodal representations, the fusion of multimodal signals at various levels, and multimodal applications. On multimodal representation learning, we review the key concept of embedding, which unifies the multimodal signals into the same vector space and thus enables cross-modality signal processing. We also review the properties of the many types of embedding constructed and learned for general downstream tasks. On multimodal fusion, this review focuses on special architectures for the integration of the representation of unimodal signals for a particular task. On applications, selected areas of a broad interest in current literature are covered, including image-to-text caption generation, text-to-image generation, and visual question answering. We believe this review can facilitate future studies in the emerging field of multimodal intelligence for the community. |
| Tasks | Image Generation, Question Answering, Representation Learning, Speech Recognition, Text-to-Image Generation, Visual Question Answering |
| Published | 2019-11-10 |
| URL | https://arxiv.org/abs/1911.03977v2 |
| https://arxiv.org/pdf/1911.03977v2.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-intelligence-representation |
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\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
| Title | \emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks |
| Authors | Bo Jiang, Zitai Zhou, Xiao Wang, Jin Tang |
| Abstract | Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality (RGB, depth) and cross-modality simultaneously which is known as cross-modality detection problem. In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (\emph{cm}SalGAN). \emph{cm}SalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem. To further improve the detection results, the attention mechanism and edge detection module are also incorporated into \emph{cm}SalGAN. The entire \emph{cm}SalGAN can be trained in an end-to-end manner by using the standard deep neural network framework. Experimental results show that \emph{cm}SalGAN achieves the new state-of-the-art RGB-D saliency detection performance on several benchmark datasets. |
| Tasks | Edge Detection, Object Detection, Saliency Detection, Salient Object Detection |
| Published | 2019-12-21 |
| URL | https://arxiv.org/abs/1912.10280v1 |
| https://arxiv.org/pdf/1912.10280v1.pdf | |
| PWC | https://paperswithcode.com/paper/emphcmsalgan-rgb-d-salient-object-detection |
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