Paper Group ANR 469
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning. Multivariate Uncertainty in Deep Learning. Is Discriminator a Good Feature Extractor?. Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis. Region-wise Generative Adversarial ImageInpainting for …
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
| Title | Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning |
| Authors | Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati |
| Abstract | In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human’s expectations about an agent may differ from the agent’s own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over existing approximate approaches that unnecessarily try to search in the space of models while also failing to facilitate the full gamut of behaviors enabled by our framework. |
| Tasks | Decision Making |
| Published | 2019-03-18 |
| URL | https://arxiv.org/abs/1903.07269v3 |
| https://arxiv.org/pdf/1903.07269v3.pdf | |
| PWC | https://paperswithcode.com/paper/planning-with-explanatory-actions-a-joint |
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Multivariate Uncertainty in Deep Learning
| Title | Multivariate Uncertainty in Deep Learning |
| Authors | Rebecca L. Russell, Christopher Reale |
| Abstract | Deep learning is increasingly used for state estimation problems such as tracking, navigation, and pose estimation. The uncertainties associated with these measurements are typically assumed to be a fixed covariance matrix. For many scenarios this assumption is inaccurate, leading to worse subsequent filtered state estimates. We show how to model multivariate uncertainty for regression problems with neural networks, incorporating both aleatoric and epistemic sources of heteroscedastic uncertainty. We train a deep uncertainty covariance matrix model in two ways: directly using a multivariate Gaussian density loss function, and indirectly using end-to-end training through a Kalman filter. We experimentally show in a visual tracking problem the large impact that accurate multivariate uncertainty quantification can have on Kalman filter estimation for both in-domain and out-of-domain evaluation data. |
| Tasks | Pose Estimation, Visual Tracking |
| Published | 2019-10-31 |
| URL | https://arxiv.org/abs/1910.14215v1 |
| https://arxiv.org/pdf/1910.14215v1.pdf | |
| PWC | https://paperswithcode.com/paper/multivariate-uncertainty-in-deep-learning |
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Is Discriminator a Good Feature Extractor?
| Title | Is Discriminator a Good Feature Extractor? |
| Authors | Xin Mao, Zhaoyu Su, Pin Siang Tan, Jun Kang Chow, Yu-Hsing Wang |
| Abstract | The discriminator from generative adversarial nets (GAN) has been used by researchers as a feature extractor in transfer learning and appeared worked well. However, there are also studies that believe this is the wrong research direction because intuitively the task of the discriminator focuses on separating the real samples from the generated ones, making features extracted in this way useless for most of the downstream tasks. To avoid this dilemma, we first conducted a thorough theoretical analysis of the relationship between the discriminator task and the features extracted. We found that the connection between the task of the discriminator and the feature is not as strong as was thought, for that the main factor restricting the feature learned by the discriminator is not the task, but is the need to prevent the entire GAN model from mode collapse during the training. From this perspective and combined with further analyses, we found that to avoid mode collapse, the features extracted by the discriminator are not guided to be different for the real samples, but divergence without noise is indeed allowed and occupies a large proportion of the feature space. This makes the features more robust and helps answer the question as to why the discriminator can succeed as a feature extractor in related research. Consequently, to expose the essence of the discriminator extractor as different from other extractors, we analyze the counterpart of the discriminator extractor, the classifier extractor that assigns the target samples to different categories. We found the performance of the discriminator extractor may be inferior to the classifier based extractor when the source classification task is similar to the target task, which is the common case, but the ability to avoid noise prevents the discriminator from being replaced by the classifier. |
| Tasks | Transfer Learning |
| Published | 2019-12-02 |
| URL | https://arxiv.org/abs/1912.00789v2 |
| https://arxiv.org/pdf/1912.00789v2.pdf | |
| PWC | https://paperswithcode.com/paper/is-discriminator-a-good-feature-extractor |
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Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
| Title | Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis |
| Authors | Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu |
| Abstract | Social media reflects the public attitudes towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities. This can define Named Entities as sentiment-bearing components. In this paper, we dive beyond Named Entities recognition to the exploitation of sentiment-annotated Named Entities in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of Named Entities based on the majority of attitudes towards them. This enabled tagging Named Entities with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that Named Entities have no considerable impact on the supervised model, while employing them in the lexicon-based model improved the classification performance and outperformed most of the baseline systems. |
| Tasks | Arabic Sentiment Analysis, Sentiment Analysis |
| Published | 2019-04-23 |
| URL | http://arxiv.org/abs/1904.10195v1 |
| http://arxiv.org/pdf/1904.10195v1.pdf | |
| PWC | https://paperswithcode.com/paper/empirical-evaluation-of-leveraging-named |
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Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
| Title | Region-wise Generative Adversarial ImageInpainting for Large Missing Areas |
| Authors | Yuqing Ma, Xianglong Liu, Shihao Bai, Lei Wang, Aishan Liu, Dacheng Tao, Edwin Hancock |
| Abstract | Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large continuous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large continuous and discontinuous missing areas. |
| Tasks | Image Inpainting |
| Published | 2019-09-27 |
| URL | https://arxiv.org/abs/1909.12507v1 |
| https://arxiv.org/pdf/1909.12507v1.pdf | |
| PWC | https://paperswithcode.com/paper/region-wise-generative-adversarial |
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An Evolutionary Framework for Automatic and Guided Discovery of Algorithms
| Title | An Evolutionary Framework for Automatic and Guided Discovery of Algorithms |
| Authors | Ruchira Sasanka, Konstantinos Krommydas |
| Abstract | This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) functions, which are challenging to design. To make evolutionary progress, instead, AAD employs Problem Guided Evolution (PGE), which requires introduction of a group of problems together. With PGE, solutions discovered for simpler problems are used to solve more complex problems in the same group. PGE also enables several new evolutionary strategies, and naturally yields to High-Performance Computing (HPC) techniques. We find that PGE and related evolutionary strategies enable AAD to discover algorithms of similar or higher complexity relative to the state-of-the-art. Specifically, AAD produces Python code for 29 array/vector problems ranging from min, max, reverse, to more challenging problems like sorting and matrix-vector multiplication. Additionally, we find that AAD shows adaptability to constrained environments/inputs and demonstrates outside-of-the-box problem solving abilities. |
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| Published | 2019-04-05 |
| URL | http://arxiv.org/abs/1904.02830v1 |
| http://arxiv.org/pdf/1904.02830v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-evolutionary-framework-for-automatic-and |
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Hierarchical Feature-Aware Tracking
| Title | Hierarchical Feature-Aware Tracking |
| Authors | Wenhua Zhang, Licheng Jiao, Jia Liu |
| Abstract | In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers. |
| Tasks | Visual Tracking |
| Published | 2019-10-13 |
| URL | https://arxiv.org/abs/1910.05751v2 |
| https://arxiv.org/pdf/1910.05751v2.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-feature-aware-correlation-filter |
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Multi-modal Predictive Models of Diabetes Progression
| Title | Multi-modal Predictive Models of Diabetes Progression |
| Authors | Ramin Ramazi, Christine Perndorfer, Emily Soriano, Jean-Philippe Laurenceau, Rahmatollah Beheshti |
| Abstract | With the increasing availability of wearable devices, continuous monitoring of individuals’ physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals’ statuses offers an unprecedented opportunity for studying complex diseases and health conditions such as type 2 diabetes (T2D). T2D is a widely common chronic disease that its roots and progression patterns are not fully understood. Predicting the progression of T2D can inform timely and more effective interventions to prevent or manage the disease. In this study, we have used a dataset related to 63 patients with T2D that includes the data from two different types of wearable devices worn by the patients: continuous glucose monitoring (CGM) devices and activity trackers (ActiGraphs). Using this dataset, we created a model for predicting the levels of four major biomarkers related to T2D after a one-year period. We developed a wide and deep neural network and used the data from the demographic information, lab tests, and wearable sensors to create the model. The deep part of our method was developed based on the long short-term memory (LSTM) structure to process the time-series dataset collected by the wearables. In predicting the patterns of the four biomarkers, we have obtained a root mean square error of 1.67% for HBA1c, 6.22 mg/dl for HDL cholesterol, 10.46 mg/dl for LDL cholesterol, and 18.38 mg/dl for Triglyceride. Compared to existing models for studying T2D, our model offers a more comprehensive tool for combining a large variety of factors that contribute to the disease. |
| Tasks | Time Series |
| Published | 2019-07-29 |
| URL | https://arxiv.org/abs/1907.12175v1 |
| https://arxiv.org/pdf/1907.12175v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-modal-predictive-models-of-diabetes |
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Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper
| Title | Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper |
| Authors | Qi Deng |
| Abstract | The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implements a two-consensus scheme to enforce upper-layer economic policies and achieve fundamental layer performance and robustness: the DPoEV incentive consensus on the application and resource layers, and the DABFT distributed consensus on the fundamental layer. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to achieve the best balance of performance, robustness, and security. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. |
| Tasks | |
| Published | 2019-09-26 |
| URL | https://arxiv.org/abs/1909.12063v1 |
| https://arxiv.org/pdf/1909.12063v1.pdf | |
| PWC | https://paperswithcode.com/paper/artificial-intelligence-blockcloud-aibc |
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A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
| Title | A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case |
| Authors | Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro |
| Abstract | A key element of understanding the efficacy of overparameterized neural networks is characterizing how they represent functions as the number of weights in the network approaches infinity. In this paper, we characterize the norm required to realize a function $f:\mathbb{R}^d\rightarrow\mathbb{R}$ as a single hidden-layer ReLU network with an unbounded number of units (infinite width), but where the Euclidean norm of the weights is bounded, including precisely characterizing which functions can be realized with finite norm. This was settled for univariate univariate functions in Savarese et al. (2019), where it was shown that the required norm is determined by the L1-norm of the second derivative of the function. We extend the characterization to multivariate functions (i.e., networks with d input units), relating the required norm to the L1-norm of the Radon transform of a (d+1)/2-power Laplacian of the function. This characterization allows us to show that all functions in Sobolev spaces $W^{s,1}(\mathbb{R})$, $s\geq d+1$, can be represented with bounded norm, to calculate the required norm for several specific functions, and to obtain a depth separation result. These results have important implications for understanding generalization performance and the distinction between neural networks and more traditional kernel learning. |
| Tasks | |
| Published | 2019-10-03 |
| URL | https://arxiv.org/abs/1910.01635v1 |
| https://arxiv.org/pdf/1910.01635v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-function-space-view-of-bounded-norm-1 |
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FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences
| Title | FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences |
| Authors | S. Mohammad Mirbagheri, Howard J. Hamilton |
| Abstract | We study the problem of classification of interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that learning classifiers are able to perform. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on five real-world datasets demonstrate the effectiveness of our methods in practice. The results provide evidence that FIBS framework effectively represents IBTSs for classification algorithms and it can even achieve better performance when the selection strategy is applied. |
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| Published | 2019-12-19 |
| URL | https://arxiv.org/abs/1912.09445v1 |
| https://arxiv.org/pdf/1912.09445v1.pdf | |
| PWC | https://paperswithcode.com/paper/fibs-a-generic-framework-for-classifying |
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A nonlocal feature-driven exemplar-based approach for image inpainting
| Title | A nonlocal feature-driven exemplar-based approach for image inpainting |
| Authors | Viktor Reshniak, Jeremy Trageser, Clayton G. Webster |
| Abstract | We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images. |
| Tasks | Feature Selection, Image Inpainting |
| Published | 2019-09-20 |
| URL | https://arxiv.org/abs/1909.09301v1 |
| https://arxiv.org/pdf/1909.09301v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-nonlocal-feature-driven-exemplar-based |
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Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge
| Title | Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge |
| Authors | Arpit Sharma |
| Abstract | The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge. This paper is under consideration for acceptance in TPLP. |
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| Published | 2019-07-25 |
| URL | https://arxiv.org/abs/1907.11112v1 |
| https://arxiv.org/pdf/1907.11112v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-answer-set-programming-for-commonsense |
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IndyLSTMs: Independently Recurrent LSTMs
| Title | IndyLSTMs: Independently Recurrent LSTMs |
| Authors | Pedro Gonnet, Thomas Deselaers |
| Abstract | We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e.\ the output and state of each LSTM cell depends on the inputs and its own output/state, as opposed to the input and the outputs/states of all the cells in the layer. The number of parameters per IndyLSTM layer, and thus the number of FLOPS per evaluation, is linear in the number of nodes in the layer, as opposed to quadratic for regular LSTM layers, resulting in potentially both smaller and faster models. We evaluate their performance experimentally by training several models on the popular \iamondb and CASIA online handwriting datasets, as well as on several of our in-house datasets. We show that IndyLSTMs, despite their smaller size, consistently outperform regular LSTMs both in terms of accuracy per parameter, and in best accuracy overall. We attribute this improved performance to the IndyLSTMs being less prone to overfitting. |
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| Published | 2019-03-19 |
| URL | http://arxiv.org/abs/1903.08023v1 |
| http://arxiv.org/pdf/1903.08023v1.pdf | |
| PWC | https://paperswithcode.com/paper/indylstms-independently-recurrent-lstms |
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Deep Generalized Convolutional Sum-Product Networks for Probabilistic Image Representations
| Title | Deep Generalized Convolutional Sum-Product Networks for Probabilistic Image Representations |
| Authors | Jos van de Wolfshaar, Andrzej Pronobis |
| Abstract | Sum-Product Networks (SPNs) are hierarchical, probabilistic graphical models capable of fast and exact inference that can be trained directly from high-dimensional, noisy data. Traditionally, SPNs struggle with capturing relationships in complex spatial data such as images. To this end, we introduce Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs), which encode spatial features through products and sums with scopes corresponding to local receptive fields. As opposed to existing convolutional SPNs, DGC-SPNs allow for overlapping convolution patches through a novel parameterization of dilation and strides, resulting in significantly improved feature coverage and feature resolution. DGC-SPNs substantially outperform other convolutional and non-convolutional SPN approaches across several visual datasets and for both generative and discriminative tasks, including image completion and image classification. In addition, we demonstrate a modificiation to hard EM learning that further improves the generative performance of DGC-SPNs. While fully probabilistic and versatile, our model is scalable and straightforward to apply in practical applications in place of traditional deep models. Our implementation is tensorized, employs efficient GPU-accelerated optimization techniques, and is available as part of an open-source library based on TensorFlow. |
| Tasks | Image Classification |
| Published | 2019-02-16 |
| URL | https://arxiv.org/abs/1902.06155v3 |
| https://arxiv.org/pdf/1902.06155v3.pdf | |
| PWC | https://paperswithcode.com/paper/deep-convolutional-sum-product-networks-for |
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