Paper Group ANR 58
The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel. Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm. Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis. Sanskrit Sandhi Splitting using seq2(s …
The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel
| Title | The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel |
| Authors | Mactar Ndaw, Macoumba Ndour, Papa Ngom |
| Abstract | In this article we study the field of Hilbertian metrics and positive definit (pd) kernels on probability measures, they have a real interest in kernel methods. Firstly we will make a study based on the Alpha-Beta-divergence to have a Hilbercan metric by proposing an improvement of this divergence by constructing it so that its is symmetrical the Alpha-Beta-Symmetric-divergence (ABS-divergence) and also do some studies on these properties but also propose the kernels associated with this divergence. Secondly we will do mumerical studies incorporating all proposed metrics/kernels into support vector machine (SVM). Finally we presented a algorithm for image classification by using our divergence. |
| Tasks | Image Classification |
| Published | 2018-03-01 |
| URL | http://arxiv.org/abs/1803.00001v2 |
| http://arxiv.org/pdf/1803.00001v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-alpha-beta-symetric-divergence-and-their |
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Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm
| Title | Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm |
| Authors | Mina Yazdandoost, Peyman Khazaei, Salar Saadatian, Rahim Kamali |
| Abstract | A new adopted evolutionary algorithm is presented in this paper to solve the non-smooth, non-convex and non-linear multi-area economic dispatch (MAED). MAED includes some areas which contains its own power generation and loads. By transmitting the power from the area with lower cost to the area with higher cost, the total cost function can be minimized greatly. The tie line capacity, multi-fuel generator and the prohibited operating zones are satisfied in this study. In addition, a new algorithm based on electro search optimization algorithm (ESOA) is proposed to solve the MAED optimization problem with considering all the constraints. In ESOA algorithm all probable moving states for individuals to get away from or move towards the worst or best solution needs to be considered. To evaluate the performance of the ESOA algorithm, the algorithm is applied to both the original economic dispatch with 40 generator systems and the multi-area economic dispatch with 3 different systems such as: 6 generators in 2 areas; and 40 generators in 4 areas. It can be concluded that, ESOA algorithm is more accurate and robust in comparison with other methods. |
| Tasks | Distributed Optimization |
| Published | 2018-05-25 |
| URL | http://arxiv.org/abs/1806.06062v1 |
| http://arxiv.org/pdf/1806.06062v1.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-optimization-strategy-for-multi |
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Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis
| Title | Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis |
| Authors | Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez, Francesca M. Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom MacGillivray, Emanuele Trucco, Joanna Wardlaw |
| Abstract | INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS: Of 111 relevant studies, most assessed Alzheimer’s disease (AD) vs healthy controls, used ADNI data, support vector machines and only T1-weighted sequences. Accuracy was highest for differentiating AD from healthy controls, and poor for differentiating healthy controls vs MCI vs AD, or MCI converters vs non-converters. Accuracy increased using combined data types, but not by data source, sample size or machine learning method. DISCUSSION: Machine learning does not differentiate clinically-relevant disease categories yet. More diverse datasets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field. |
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| Published | 2018-04-05 |
| URL | http://arxiv.org/abs/1804.01961v2 |
| http://arxiv.org/pdf/1804.01961v2.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-of-neuroimaging-to-diagnose |
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Sanskrit Sandhi Splitting using seq2(seq)^2
| Title | Sanskrit Sandhi Splitting using seq2(seq)^2 |
| Authors | Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani |
| Abstract | In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well. |
| Tasks | Chinese Word Segmentation |
| Published | 2018-01-01 |
| URL | https://arxiv.org/abs/1801.00428v4 |
| https://arxiv.org/pdf/1801.00428v4.pdf | |
| PWC | https://paperswithcode.com/paper/sanskrit-sandhi-splitting-using-pmbseq2seq2 |
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A Framework and Method for Online Inverse Reinforcement Learning
| Title | A Framework and Method for Online Inverse Reinforcement Learning |
| Authors | Saurabh Arora, Prashant Doshi, Bikramjit Banerjee |
| Abstract | Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {\em online} IRL—where the observations are incrementally accrued, yet the demands of the application often prohibit a full rerun of an IRL method—has received relatively less attention. We introduce the first formal framework for online IRL, called incremental IRL (I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our formal analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application of penetrating a continuous patrol under occlusion shows the relatively improved performance and speed up of the new method and validates the utility of online IRL. |
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| Published | 2018-05-21 |
| URL | http://arxiv.org/abs/1805.07871v1 |
| http://arxiv.org/pdf/1805.07871v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-framework-and-method-for-online-inverse |
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Improving Simple Models with Confidence Profiles
| Title | Improving Simple Models with Confidence Profiles |
| Authors | Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder Olsen |
| Abstract | In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretable model or a very shallow network of low complexity and a priori low test accuracy. We are motivated by applications in interpretability and model deployment in severely memory constrained environments (like sensors). Our method uses linear probes to generate confidence scores through flattened intermediate representations. Our transfer method involves a theoretically justified weighting of samples during the training of the simple model using confidence scores of these intermediate layers. The value of our method is first demonstrated on CIFAR-10, where our weighting method significantly improves (3-4%) networks with only a fraction of the number of Resnet blocks of a complex Resnet model. We further demonstrate operationally significant results on a real manufacturing problem, where we dramatically increase the test accuracy of a CART model (the domain standard) by roughly 13%. |
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| Published | 2018-07-19 |
| URL | http://arxiv.org/abs/1807.07506v2 |
| http://arxiv.org/pdf/1807.07506v2.pdf | |
| PWC | https://paperswithcode.com/paper/improving-simple-models-with-confidence |
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An Extension of Averaged-Operator-Based Algorithms
| Title | An Extension of Averaged-Operator-Based Algorithms |
| Authors | Miguel Simões, José Bioucas-Dias, Luis B. Almeida |
| Abstract | Many of the algorithms used to solve minimization problems with sparsity-inducing regularizers are generic in the sense that they do not take into account the sparsity of the solution in any particular way. However, algorithms known as semismooth Newton are able to take advantage of this sparsity to accelerate their convergence. We show how to extend these algorithms in different directions, and study the convergence of the resulting algorithms by showing that they are a particular case of an extension of the well-known Krasnosel’ski\u{\i}–Mann scheme. |
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| Published | 2018-06-12 |
| URL | http://arxiv.org/abs/1806.04561v1 |
| http://arxiv.org/pdf/1806.04561v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-extension-of-averaged-operator-based |
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Neural source-filter-based waveform model for statistical parametric speech synthesis
| Title | Neural source-filter-based waveform model for statistical parametric speech synthesis |
| Authors | Xin Wang, Shinji Takaki, Junichi Yamagishi |
| Abstract | Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distilling training method and the blend of other disparate training criteria. This study proposes a non-AR neural source-filter waveform model that can be directly trained using spectrum-based training criteria and the stochastic gradient descent method. Given the input acoustic features, the proposed model first uses a source module to generate a sine-based excitation signal and then uses a filter module to transform the excitation signal into the output speech waveform. Our experiments demonstrated that the proposed model generated waveforms at least 100 times faster than the AR WaveNet and the quality of its synthetic speech is close to that of speech generated by the AR WaveNet. Ablation test results showed that both the sine-wave excitation signal and the spectrum-based training criteria were essential to the performance of the proposed model. |
| Tasks | Speech Synthesis |
| Published | 2018-10-29 |
| URL | http://arxiv.org/abs/1810.11946v4 |
| http://arxiv.org/pdf/1810.11946v4.pdf | |
| PWC | https://paperswithcode.com/paper/neural-source-filter-based-waveform-model-for |
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A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI
| Title | A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI |
| Authors | Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui |
| Abstract | Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions. |
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| Published | 2018-02-08 |
| URL | http://arxiv.org/abs/1802.02925v2 |
| http://arxiv.org/pdf/1802.02925v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-deep-unsupervised-learning-approach-toward |
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The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
| Title | The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers |
| Authors | Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen |
| Abstract | Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the1960s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated us to present a comprehensive survey to deliver a clear and complete picture of automatic math problem solvers. In this survey, we emphasize on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap and compare their performance in the publicly accessible datasets. We also cover automatic solvers for other types of math problems such as geometric problems that require the understanding of diagrams. Finally, we identify several emerging research directions for the readers with interests in MWPs. |
| Tasks | Semantic Parsing |
| Published | 2018-08-22 |
| URL | http://arxiv.org/abs/1808.07290v2 |
| http://arxiv.org/pdf/1808.07290v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-gap-of-semantic-parsing-a-survey-on |
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Rethinking Epistemic Logic with Belief Bases
| Title | Rethinking Epistemic Logic with Belief Bases |
| Authors | Emiliano Lorini |
| Abstract | We introduce a new semantics for a logic of explicit and implicit beliefs based on the concept of multi-agent belief base. Differently from existing Kripke-style semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are defined from the concept of belief base. We provide a complete axiomatization and prove decidability for our logic via a finite model argument. We also provide a polynomial embedding of our logic into Fagin & Halpern’s logic of general awareness and establish a complexity result for our logic via the embedding. |
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| Published | 2018-12-17 |
| URL | http://arxiv.org/abs/1812.07079v1 |
| http://arxiv.org/pdf/1812.07079v1.pdf | |
| PWC | https://paperswithcode.com/paper/rethinking-epistemic-logic-with-belief-bases |
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Anaphora and Coreference Resolution: A Review
| Title | Anaphora and Coreference Resolution: A Review |
| Authors | Rhea Sukthanker, Soujanya Poria, Erik Cambria, Ramkumar Thirunavukarasu |
| Abstract | Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research. This field possesses immense potential to improve the performance of other NLP fields like machine translation, sentiment analysis, paraphrase detection, summarization, etc. The area of entity resolution in NLP has seen proliferation of research in two separate sub-areas namely: anaphora resolution and coreference resolution. Through this review article, we aim at clarifying the scope of these two tasks in entity resolution. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle this NLP problem. This survey is motivated with the aim of providing the reader with a clear understanding of what constitutes this NLP problem and the issues that require attention. |
| Tasks | Coreference Resolution, Entity Resolution, Machine Translation, Sentiment Analysis |
| Published | 2018-05-30 |
| URL | http://arxiv.org/abs/1805.11824v1 |
| http://arxiv.org/pdf/1805.11824v1.pdf | |
| PWC | https://paperswithcode.com/paper/anaphora-and-coreference-resolution-a-review |
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Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
| Title | Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts |
| Authors | Ran Tian, Sisi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard, Yildiray Yildiz |
| Abstract | In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported. |
| Tasks | Decision Making |
| Published | 2018-10-01 |
| URL | http://arxiv.org/abs/1810.00829v1 |
| http://arxiv.org/pdf/1810.00829v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-game-theoretic-decision-making-for |
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Chinese Poetry Generation with a Salient-Clue Mechanism
| Title | Chinese Poetry Generation with a Salient-Clue Mechanism |
| Authors | Xiaoyuan Yi, Ruoyu Li, Maosong Sun |
| Abstract | As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines. |
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| Published | 2018-09-12 |
| URL | http://arxiv.org/abs/1809.04313v1 |
| http://arxiv.org/pdf/1809.04313v1.pdf | |
| PWC | https://paperswithcode.com/paper/chinese-poetry-generation-with-a-salient-clue |
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Scenarios: A New Representation for Complex Scene Understanding
| Title | Scenarios: A New Representation for Complex Scene Understanding |
| Authors | Zachary A. Daniels, Dimitris N. Metaxas |
| Abstract | The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at addressing the problem of complex scene understanding lack representational power, efficiency, and the ability to create robust meta-knowledge about scenes. In this paper, we introduce scenarios as a new way of representing scenes. The scenario is a simple, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects and is useful for a wide range of scene understanding tasks. We learn scenarios from data using a novel matrix factorization method which we integrate into a new neural network architecture, the ScenarioNet. Using ScenarioNet, we can recover semantic information about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classification, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. In addition to solving many tasks in a single, unified framework, ScenarioNet is more computationally efficient than other CNNs because it requires significantly fewer parameters while achieving similar performance on benchmark tasks and is more interpretable because it produces explanations when making decisions. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets. |
| Tasks | Image Retrieval, Object Recognition, Scene Classification, Scene Understanding |
| Published | 2018-02-16 |
| URL | http://arxiv.org/abs/1802.06117v1 |
| http://arxiv.org/pdf/1802.06117v1.pdf | |
| PWC | https://paperswithcode.com/paper/scenarios-a-new-representation-for-complex |
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