Paper Group ANR 270
Sharpe Ratio in High Dimensions: Cases of Maximum Out of Sample, Constrained Maximum, and Optimal Portfolio Choice. A Comparative Evaluation of Pitch Modification Techniques. Incremental Evolution and Development of Deep Artificial Neural Networks. Impact of Semantic Granularity on Geographic Information Search Support. Evaluation of Model Selectio …
Sharpe Ratio in High Dimensions: Cases of Maximum Out of Sample, Constrained Maximum, and Optimal Portfolio Choice
| Title | Sharpe Ratio in High Dimensions: Cases of Maximum Out of Sample, Constrained Maximum, and Optimal Portfolio Choice |
| Authors | Mehmet Caner, Marcelo Medeiros, Gabriel Vasconcelos |
| Abstract | In this paper, we analyze maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the excess asset returns. To solve this issue, we benefit from a technique called nodewise regression, which was developed by Meinshausen and Buhlmann (2006). It provides a sparse/weakly sparse and consistent estimate of the precision matrix, using the Lasso method. We analyze three issues. One of the key results in our paper is that mean-variance efficiency for the portfolios in large dimensions is established. Then tied to that result, we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets. Furthermore, we provide convergence rates and see that the number of assets slow down the convergence up to a logarithmic factor. Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio. Finally, we provide consistent estimates of the Sharpe ratios of global minimum variance portfolio and Markowitz’s (1952) mean variance portfolio. In terms of assumptions, we allow for time series data. Simulation and out-of-sample forecasting exercise shows that our new method performs well compared to factor and shrinkage based techniques. |
| Tasks | Time Series |
| Published | 2020-02-05 |
| URL | https://arxiv.org/abs/2002.01800v1 |
| https://arxiv.org/pdf/2002.01800v1.pdf | |
| PWC | https://paperswithcode.com/paper/sharpe-ratio-in-high-dimensions-cases-of |
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A Comparative Evaluation of Pitch Modification Techniques
| Title | A Comparative Evaluation of Pitch Modification Techniques |
| Authors | Thomas Drugman, Thierry Dutoit |
| Abstract | This paper addresses the problem of pitch modification, as an important module for an efficient voice transformation system. The Deterministic plus Stochastic Model of the residual signal we proposed in a previous work is compared to TDPSOLA, HNM and STRAIGHT. The four methods are compared through an important subjective test. The influence of the speaker gender and of the pitch modification ratio is analyzed. Despite its higher compression level, the DSM technique is shown to give similar or better results than other methods, especially for male speakers and important ratios of modification. The DSM turns out to be only outperformed by STRAIGHT for female voices. |
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| Published | 2020-01-02 |
| URL | https://arxiv.org/abs/2001.00579v1 |
| https://arxiv.org/pdf/2001.00579v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparative-evaluation-of-pitch |
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Incremental Evolution and Development of Deep Artificial Neural Networks
| Title | Incremental Evolution and Development of Deep Artificial Neural Networks |
| Authors | Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado |
| Abstract | NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks(ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems. |
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| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00302v1 |
| https://arxiv.org/pdf/2004.00302v1.pdf | |
| PWC | https://paperswithcode.com/paper/incremental-evolution-and-development-of-deep |
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Impact of Semantic Granularity on Geographic Information Search Support
| Title | Impact of Semantic Granularity on Geographic Information Search Support |
| Authors | Noemi Mauro, Liliana Ardissono, Laura Di Rocco, Michela Bertolotto, Giovanna Guerrini |
| Abstract | The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts representation) on the suggestion of relevant types of information to search for. We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space. A comparative analysis of the performance of a query expansion model, using three spatial ontologies defined at different semantic granularity levels, reveals that a fine-grained representation enhances recall. However, precision depends on how closely the ontologies match the way people conceptualize and verbally describe the geographic space. |
| Tasks | Information Retrieval |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00293v1 |
| https://arxiv.org/pdf/2004.00293v1.pdf | |
| PWC | https://paperswithcode.com/paper/impact-of-semantic-granularity-on-geographic |
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Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage
| Title | Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage |
| Authors | Christoffer Bøgelund Rasmussen, Thomas B. Moeslund |
| Abstract | Model selection when designing deep learning systems for specific use-cases can be a challenging task as many options exist and it can be difficult to know the trade-off between them. Therefore, we investigate a number of state of the art CNN models for the task of measuring kernel fragmentation in harvested corn silage. The models are evaluated across a number of feature extractors and image sizes in order to determine optimal model design choices based upon the trade-off between model complexity, accuracy and speed. We show that accuracy improvements can be made with more complex meta-architectures and speed can be optimised by decreasing the image size with only slight losses in accuracy. Additionally, we show improvements in Average Precision at an Intersection over Union of 0.5 of up to 20 percentage points while also decreasing inference time in comparison to previously published work. This result for better model selection enables opportunities for creating systems that can aid farmers in improving their silage quality while harvesting. |
| Tasks | Model Selection |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00292v1 |
| https://arxiv.org/pdf/2004.00292v1.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-of-model-selection-for-kernel |
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Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective
| Title | Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective |
| Authors | Tine Van Calster, Filip Van den Bossche, Bart Baesens, Wilfried Lemahieu |
| Abstract | Choosing the technique that is the best at forecasting your data, is a problem that arises in any forecasting application. Decades of research have resulted into an enormous amount of forecasting methods that stem from statistics, econometrics and machine learning (ML), which leads to a very difficult and elaborate choice to make in any forecasting exercise. This paper aims to facilitate this process for high-level tactical sales forecasts by comparing a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets. However, instead of solely focusing on the accuracy of the resulting forecasts, this paper introduces a novel and completely automated profit-driven approach that takes into account the expected profit that a technique can create during both the model building and evaluation process. The expected profit function that is used for this purpose, is easy to understand and adaptable to any situation by combining forecasting accuracy with business expertise. Furthermore, we examine the added value of ML techniques, the inclusion of external factors and the use of seasonal models in order to ascertain which type of model works best in tactical sales forecasting. Our findings show that simple seasonal time series models consistently outperform other methodologies and that the profit-driven approach can lead to selecting a different forecasting model. |
| Tasks | Time Series |
| Published | 2020-02-03 |
| URL | https://arxiv.org/abs/2002.00949v1 |
| https://arxiv.org/pdf/2002.00949v1.pdf | |
| PWC | https://paperswithcode.com/paper/profit-oriented-sales-forecasting-a |
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Recommandation ontologique multicritère pour la métrologie
| Title | Recommandation ontologique multicritère pour la métrologie |
| Authors | Axel Mascaro, Christophe Rey |
| Abstract | Matchmaking and information ranking are helping process for users, by offering them the best answers possible at their request. When there is no exact answer, giving them the closest proposition available is an efficient upgrade of that helping process. With a reasearch platform on metrology as a framework, we will discuss about ranking with knowledge representation, with an approach based on Description Logic, ontologies and multricriteria comparison. We present a reasonning to compare each proposition with the other, with semantic and syntaxic difference, by troncating the information in distinct component. |
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| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00291v1 |
| https://arxiv.org/pdf/2004.00291v1.pdf | |
| PWC | https://paperswithcode.com/paper/recommandation-ontologique-multicritere-pour |
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CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
| Title | CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition |
| Authors | Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang |
| Abstract | As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors. |
| Tasks | Face Recognition |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00288v1 |
| https://arxiv.org/pdf/2004.00288v1.pdf | |
| PWC | https://paperswithcode.com/paper/curricularface-adaptive-curriculum-learning |
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A generalised OMP algorithm for feature selection with application to gene expression data
| Title | A generalised OMP algorithm for feature selection with application to gene expression data |
| Authors | Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos |
| Abstract | Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest. To apply to molecular data, feature selection algorithms need to be scalable to tens of thousands of available features. In this paper, we propose gOMP, a highly-scalable generalisation of the Orthogonal Matching Pursuit feature selection algorithm to several directions: (a) different types of outcomes, such as continuous, binary, nominal, and time-to-event, (b) different types of predictive models (e.g., linear least squares, logistic regression), (c) different types of predictive features (continuous, categorical), and (d) different, statistical-based stopping criteria. We compare the proposed algorithm against LASSO, a prototypical, widely used algorithm for high-dimensional data. On dozens of simulated datasets, as well as, real gene expression datasets, gOMP is on par, or outperforms LASSO for case-control binary classification, quantified outcomes (regression), and (censored) survival times (time-to-event) analysis. gOMP has also several theoretical advantages that are discussed. While gOMP is based on quite simple and basic statistical ideas, easy to implement and to generalize, we also show in an extensive evaluation that it is also quite effective in bioinformatics analysis settings. |
| Tasks | Feature Selection |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00281v1 |
| https://arxiv.org/pdf/2004.00281v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-generalised-omp-algorithm-for-feature |
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Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
| Title | Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing |
| Authors | Hengtong Hu, Lingxi Xie, Richang Hong, Qi Tian |
| Abstract | In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval. There are two main frameworks for CMH, differing from each other in whether semantic supervision is required. Compared to the unsupervised methods, the supervised methods often enjoy more accurate results, but require much heavier labors in data annotation. In this paper, we propose a novel approach that enables guiding a supervised method using outputs produced by an unsupervised method. Specifically, we make use of teacher-student optimization for propagating knowledge. Experiments are performed on two popular CMH benchmarks, i.e., the MIRFlickr and NUS-WIDE datasets. Our approach outperforms all existing unsupervised methods by a large margin. |
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| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00280v1 |
| https://arxiv.org/pdf/2004.00280v1.pdf | |
| PWC | https://paperswithcode.com/paper/creating-something-from-nothing-unsupervised |
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Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost
| Title | Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost |
| Authors | Adria Ruiz, Jakob Verbeek |
| Abstract | Deep neural networks form the basis of state-of-the-art models across a variety of application domains. Moreover, networks that are able to dynamically adapt the computational cost of inference are important in scenarios where the amount of compute or input data varies over time. In this paper, we propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks by sharing intermediate layers using a hierarchical structure. In HNE we control the inference cost by evaluating only a subset of models, which are organized in a nested manner. Our second contribution is a novel co-distillation method to boost the performance of ensemble predictions with low inference cost. This approach leverages the nested structure of our ensembles, to optimally allocate accuracy and diversity across the ensemble members. Comprehensive experiments over the CIFAR and ImageNet datasets confirm the effectiveness of HNE in building deep networks with adaptive inference cost for image classification. |
| Tasks | Image Classification |
| Published | 2020-03-03 |
| URL | https://arxiv.org/abs/2003.01474v2 |
| https://arxiv.org/pdf/2003.01474v2.pdf | |
| PWC | https://paperswithcode.com/paper/distilled-hierarchical-neural-ensembles-with |
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Differential Privacy for Sequential Algorithms
| Title | Differential Privacy for Sequential Algorithms |
| Authors | Yu Wang, Hussein Sibai, Sayan Mitra, Geir E. Dullerud |
| Abstract | We study the differential privacy of sequential statistical inference and learning algorithms that are characterized by random termination time. Using the two examples: sequential probability ratio test and sequential empirical risk minimization, we show that the number of steps such algorithms execute before termination can jeopardize the differential privacy of the input data in a similar fashion as their outputs, and it is impossible to use the usual Laplace mechanism to achieve standard differentially private in these examples. To remedy this, we propose a notion of weak differential privacy and demonstrate its equivalence to the standard case for large i.i.d. samples. We show that using the Laplace mechanism, weak differential privacy can be achieved for both the sequential probability ratio test and the sequential empirical risk minimization with proper performance guarantees. Finally, we provide preliminary experimental results on the Breast Cancer Wisconsin (Diagnostic) and Landsat Satellite Data Sets from the UCI repository. |
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| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00275v1 |
| https://arxiv.org/pdf/2004.00275v1.pdf | |
| PWC | https://paperswithcode.com/paper/differential-privacy-for-sequential |
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Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
| Title | Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning |
| Authors | Yu Wang, Nima Roohi, Matthew West, Mahesh Viswanathan, Geir E. Dullerud |
| Abstract | Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety. In this work, we focus on model checking PCTL specifications statistically on Markov Decision Processes (MDPs) by sampling, e.g., checking whether there exists a feasible policy such that the probability of reaching certain goal states is greater than a threshold. We use reinforcement learning to search for such a feasible policy for PCTL specifications, and then develop a statistical model checking (SMC) method with provable guarantees on its error. Specifically, we first use upper-confidence-bound (UCB) based Q-learning to design an SMC algorithm for bounded-time PCTL specifications, and then extend this algorithm to unbounded-time specifications by identifying a proper truncation time by checking the PCTL specification and its negation at the same time. Finally, we evaluate the proposed method on case studies. |
| Tasks | Q-Learning |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00273v1 |
| https://arxiv.org/pdf/2004.00273v1.pdf | |
| PWC | https://paperswithcode.com/paper/statistically-model-checking-pctl |
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An Efficient Agreement Mechanism in CapsNets By Pairwise Product
| Title | An Efficient Agreement Mechanism in CapsNets By Pairwise Product |
| Authors | Lei Zhao, Xiaohui Wang, Lei Huang |
| Abstract | Capsule networks (CapsNets) are capable of modeling visual hierarchical relationships, which is achieved by the “routing-by-agreement” mechanism. This paper proposes a pairwise agreement mechanism to build capsules, inspired by the feature interactions of factorization machines (FMs). The proposed method has a much lower computation complexity. We further proposed a new CapsNet architecture that combines the strengths of residual networks in representing low-level visual features and CapsNets in modeling the relationships of parts to wholes. We conduct comprehensive experiments to compare the routing algorithms, including dynamic routing, EM routing, and our proposed FM agreement, based on both architectures of original CapsNet and our proposed one, and the results show that our method achieves both excellent performance and efficiency under a variety of situations. |
| Tasks | |
| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00272v1 |
| https://arxiv.org/pdf/2004.00272v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-efficient-agreement-mechanism-in-capsnets |
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Map-Based Visualization of 2D/3D Spatial Data via Stylization and Tuning of Information Emphasis
| Title | Map-Based Visualization of 2D/3D Spatial Data via Stylization and Tuning of Information Emphasis |
| Authors | Liliana Ardissono, Matteo Delsanto, Maurizio Lucenteforte, Noemi Mauro, Adriano Savoca, Daniele Scanu |
| Abstract | In Geographical Information search, map visualization can challenge the user because results can consist of a large set of heterogeneous items, increasing visual complexity. We propose a novel visualization model to address this issue. Our model represents results as markers, or as geometric objects, on 2D/3D layers, using stylized and highly colored shapes to enhance their visibility. Moreover, the model supports interactive information filtering in the map by enabling the user to focus on different data categories, using transparency sliders to tune the opacity, and thus the emphasis, of the corresponding data items. A test with users provided positive results concerning the efficacy of the model. |
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| Published | 2020-04-01 |
| URL | https://arxiv.org/abs/2004.00267v1 |
| https://arxiv.org/pdf/2004.00267v1.pdf | |
| PWC | https://paperswithcode.com/paper/map-based-visualization-of-2d-3d-spatial-data |
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