Paper Group ANR 668
Multilevel Context Representation for Improving Object Recognition. Enabling Reasoning with LegalRuleML. Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars. Stock Prediction: a method based on extraction of news features and recurrent neural networks. Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with …
Multilevel Context Representation for Improving Object Recognition
| Title | Multilevel Context Representation for Improving Object Recognition |
| Authors | Andreas Kölsch, Muhammad Zeshan Afzal, Marcus Liwicki |
| Abstract | In this work, we propose the combined usage of low- and high-level blocks of convolutional neural networks (CNNs) for improving object recognition. While recent research focused on either propagating the context from all layers, e.g. ResNet, (including the very low-level layers) or having multiple loss layers (e.g. GoogLeNet), the importance of the features close to the higher layers is ignored. This paper postulates that the use of context closer to the high-level layers provides the scale and translation invariance and works better than using the top layer only. In particular, we extend AlexNet and GoogLeNet by additional connections in the top $n$ layers. In order to demonstrate the effectiveness of the proposed approach, we evaluated it on the standard ImageNet task. The relative reduction of the classification error is around 1-2% without affecting the computational cost. Furthermore, we show that this approach is orthogonal to typical test data augmentation techniques, as recently introduced by Szegedy et al. (leading to a runtime reduction of 144 during test time). |
| Tasks | Data Augmentation, Object Recognition |
| Published | 2017-03-19 |
| URL | http://arxiv.org/abs/1703.06408v1 |
| http://arxiv.org/pdf/1703.06408v1.pdf | |
| PWC | https://paperswithcode.com/paper/multilevel-context-representation-for |
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Enabling Reasoning with LegalRuleML
| Title | Enabling Reasoning with LegalRuleML |
| Authors | Ho-Pun Lam, Mustafa Hashmi |
| Abstract | In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client’s preferences. |
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| Published | 2017-11-11 |
| URL | http://arxiv.org/abs/1711.06128v2 |
| http://arxiv.org/pdf/1711.06128v2.pdf | |
| PWC | https://paperswithcode.com/paper/enabling-reasoning-with-legalruleml |
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Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
| Title | Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars |
| Authors | Lukas Hewing, Alexander Liniger, Melanie N. Zeilinger |
| Abstract | This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Process (GP) and takes residual model uncertainty into account through a chance constrained formulation. We present a sparse GP approximation with dynamically adjusting inducing inputs, enabling a real-time implementable controller. The formulation is demonstrated in simulations, which show significant improvement with respect to both lap time and constraint satisfaction compared to an NMPC without model learning. |
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| Published | 2017-11-17 |
| URL | http://arxiv.org/abs/1711.06586v2 |
| http://arxiv.org/pdf/1711.06586v2.pdf | |
| PWC | https://paperswithcode.com/paper/cautious-nmpc-with-gaussian-process-dynamics |
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Stock Prediction: a method based on extraction of news features and recurrent neural networks
| Title | Stock Prediction: a method based on extraction of news features and recurrent neural networks |
| Authors | Zeya Zhang, Weizheng Chen, Hongfei Yan |
| Abstract | This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the positive polar of their words. In consideration of sequential stock prices and continuous news effects, we propose a recurrent neural network model to help predict stock prices. Compared to SVM classifier with price features, we find our proposed method has an over 5% improvement on stock prediction accuracy in experiments. |
| Tasks | Stock Prediction |
| Published | 2017-07-19 |
| URL | http://arxiv.org/abs/1707.07585v1 |
| http://arxiv.org/pdf/1707.07585v1.pdf | |
| PWC | https://paperswithcode.com/paper/stock-prediction-a-method-based-on-extraction |
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Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading
| Title | Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading |
| Authors | Qi Liu-Yin, Rui Yu, Lourdes Agapito, Andrew Fitzgibbon, Chris Russell |
| Abstract | We demonstrate the use of shape-from-shading (SfS) to improve both the quality and the robustness of 3D reconstruction of dynamic objects captured by a single camera. Unlike previous approaches that made use of SfS as a post-processing step, we offer a principled integrated approach that solves dynamic object tracking and reconstruction and SfS as a single unified cost function. Moving beyond Lambertian S f S , we propose a general approach that models both specularities and shading while simultaneously tracking and reconstructing general dynamic objects. Solving these problems jointly prevents the kinds of tracking failures which can not be recovered from by pipeline approaches. We show state-of-the-art results both qualitatively and quantitatively. |
| Tasks | 3D Reconstruction, Object Tracking |
| Published | 2017-08-04 |
| URL | http://arxiv.org/abs/1708.01654v1 |
| http://arxiv.org/pdf/1708.01654v1.pdf | |
| PWC | https://paperswithcode.com/paper/better-together-joint-reasoning-for-non-rigid |
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A retrieval-based dialogue system utilizing utterance and context embeddings
| Title | A retrieval-based dialogue system utilizing utterance and context embeddings |
| Authors | Alexander Bartl, Gerasimos Spanakis |
| Abstract | Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the “meaning” of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system. |
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| Published | 2017-10-16 |
| URL | http://arxiv.org/abs/1710.05780v3 |
| http://arxiv.org/pdf/1710.05780v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-retrieval-based-dialogue-system-utilizing |
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Region-based Quality Estimation Network for Large-scale Person Re-identification
| Title | Region-based Quality Estimation Network for Large-scale Person Re-identification |
| Authors | Guanglu Song, Biao Leng, Yu Liu, Congrui Hetang, Shaofan Cai |
| Abstract | One of the major restrictions on the performance of video-based person re-id is partial noise caused by occlusion, blur and illumination. Since different spatial regions of a single frame have various quality, and the quality of the same region also varies across frames in a tracklet, a good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate the influence of an image region with poor quality. To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Compared with other feature extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83% on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate the lack of clean large-scale person re-id datasets for the community, this paper also contributes a new high-quality dataset, named “Labeled Pedestrian in the Wild (LPW)” which contains 7,694 tracklets with over 590,000 images. Despite its relatively large scale, the annotations also possess high cleanliness. Moreover, it’s more challenging in the following aspects: the age of characters varies from childhood to elderhood; the postures of people are diverse, including running and cycling in addition to the normal walking state. |
| Tasks | Large-Scale Person Re-Identification, Person Re-Identification |
| Published | 2017-11-23 |
| URL | http://arxiv.org/abs/1711.08766v2 |
| http://arxiv.org/pdf/1711.08766v2.pdf | |
| PWC | https://paperswithcode.com/paper/region-based-quality-estimation-network-for |
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Online Learning with Gated Linear Networks
| Title | Online Learning with Gated Linear Networks |
| Authors | Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth |
| Abstract | This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our method gains representational power by the use of data conditioning. We state under general conditions a learnable capacity theorem that shows this approach can in principle learn any bounded Borel-measurable function on a compact subset of euclidean space; the result is stronger than many universality results for connectionist architectures because we provide both the model and the learning procedure for which convergence is guaranteed. |
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| Published | 2017-12-05 |
| URL | http://arxiv.org/abs/1712.01897v1 |
| http://arxiv.org/pdf/1712.01897v1.pdf | |
| PWC | https://paperswithcode.com/paper/online-learning-with-gated-linear-networks |
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Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
| Title | Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks |
| Authors | Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio |
| Abstract | A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states. |
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| Published | 2017-11-07 |
| URL | http://arxiv.org/abs/1711.02326v1 |
| http://arxiv.org/pdf/1711.02326v1.pdf | |
| PWC | https://paperswithcode.com/paper/sparse-attentive-backtracking-long-range |
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Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey
| Title | Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey |
| Authors | Morten Grønnesby, Juan Carlos Aviles Solis, Einar Holsbø, Hasse Melbye, Lars Ailo Bongo |
| Abstract | In recent years, many innovative solutions for recording and viewing sounds from a stethoscope have become available. However, to fully utilize such devices, there is a need for an automated approach for detecting abnormal lung sounds, which is better than the existing methods that typically have been developed and evaluated using a small and non-diverse dataset. We propose a machine learning based approach for detecting crackles in lung sounds recorded using a stethoscope in a large health survey. Our method is trained and evaluated using 209 files with crackles classified by expert listeners. Our analysis pipeline is based on features extracted from small windows in audio files. We evaluated several feature extraction methods and classifiers. We evaluated the pipeline using a training set of 175 crackle windows and 208 normal windows. We did 100 cycles of cross validation where we shuffled training sets between cycles. For all the division between training and evaluation was 70%-30%. We found and evaluated a 5-dimenstional vector with four features from the time domain and one from the spectrum domain. We evaluated several classifiers and found SVM with a Radial Basis Function Kernel to perform best. Our approach had a precision of 86% and recall of 84% for classifying a crackle in a window, which is more accurate than found in studies of health personnel. The low-dimensional feature vector makes the SVM very fast. The model can be trained on a regular computer in 1.44 seconds, and 319 crackles can be classified in 1.08 seconds. Our approach detects and visualizes individual crackles in recorded audio files. It is accurate, fast, and has low resource requirements. It can be used to train health personnel or as part of a smartphone application for Bluetooth stethoscopes. |
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| Published | 2017-05-31 |
| URL | http://arxiv.org/abs/1706.00005v2 |
| http://arxiv.org/pdf/1706.00005v2.pdf | |
| PWC | https://paperswithcode.com/paper/feature-extraction-for-machine-learning-based |
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Neural Programming by Example
| Title | Neural Programming by Example |
| Authors | Chengxun Shu, Hongyu Zhang |
| Abstract | Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to-end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs. |
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| Published | 2017-03-15 |
| URL | http://arxiv.org/abs/1703.04990v1 |
| http://arxiv.org/pdf/1703.04990v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-programming-by-example |
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HLA class I binding prediction via convolutional neural networks
| Title | HLA class I binding prediction via convolutional neural networks |
| Authors | Yeeleng Scott Vang, Xiaohui Xie |
| Abstract | Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases. We apply machine learning techniques from the natural language processing (NLP) domain to address the task of MHC-peptide binding prediction. More specifically, we introduce a new distributed representation of amino acids, name HLA-Vec, that can be used for a variety of downstream proteomic machine learning tasks. We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture achieves state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding. |
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| Published | 2017-01-03 |
| URL | http://arxiv.org/abs/1701.00593v2 |
| http://arxiv.org/pdf/1701.00593v2.pdf | |
| PWC | https://paperswithcode.com/paper/hla-class-i-binding-prediction-via |
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Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks
| Title | Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks |
| Authors | Najeeb Khan, Ian Stavness |
| Abstract | The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement. Muscle redundancy makes motor control a challenging task: there are many possible activation trajectories that accomplish the same movement. Despite this redundancy, most movements are accomplished in highly stereotypical ways. For example, point-to-point reaching movements are almost universally performed with very similar smooth trajectories. Optimization methods are commonly used to predict muscle forces for measured movements. However, these approaches require computationally expensive simulations and are sensitive to the chosen optimality criteria and regularization. In this work, we investigate deep autoencoders for the prediction of muscle activation trajectories for point-to-point reaching movements. We evaluate our DNN predictions with simulated reaches and two methods to generate the muscle activations: inverse dynamics (ID) and optimal control (OC) criteria. We also investigate optimal network parameters and training criteria to improve the accuracy of the predictions. |
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| Published | 2017-06-13 |
| URL | http://arxiv.org/abs/1706.04145v1 |
| http://arxiv.org/pdf/1706.04145v1.pdf | |
| PWC | https://paperswithcode.com/paper/prediction-of-muscle-activations-for-reaching |
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FSITM: A Feature Similarity Index For Tone-Mapped Images
| Title | FSITM: A Feature Similarity Index For Tone-Mapped Images |
| Authors | Hossein Ziaei Nafchi, Atena Shahkolaei, Reza Farrahi Moghaddam, Mohamed Cheriet |
| Abstract | In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices. The MATLAB source code of the proposed metric(s) is available at https://www.mathworks.com/matlabcentral/fileexchange/59814. |
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| Published | 2017-04-19 |
| URL | http://arxiv.org/abs/1704.05624v1 |
| http://arxiv.org/pdf/1704.05624v1.pdf | |
| PWC | https://paperswithcode.com/paper/fsitm-a-feature-similarity-index-for-tone |
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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
| Title | Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization |
| Authors | Jonathan Scarlett, Ilijia Bogunovic, Volkan Cevher |
| Abstract | In this paper, we consider the problem of sequentially optimizing a black-box function $f$ based on noisy samples and bandit feedback. We assume that $f$ is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian process bandit optimization. We provide algorithm-independent lower bounds on the simple regret, measuring the suboptimality of a single point reported after $T$ rounds, and on the cumulative regret, measuring the sum of regrets over the $T$ chosen points. For the isotropic squared-exponential kernel in $d$ dimensions, we find that an average simple regret of $\epsilon$ requires $T = \Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}} \big)$, thus matching existing upper bounds up to the replacement of $d/2$ by $2d+O(1)$ in both cases. For the Mat'ern-$\nu$ kernel, we give analogous bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and $\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting gaps to the existing upper bounds. |
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| Published | 2017-05-31 |
| URL | http://arxiv.org/abs/1706.00090v3 |
| http://arxiv.org/pdf/1706.00090v3.pdf | |
| PWC | https://paperswithcode.com/paper/lower-bounds-on-regret-for-noisy-gaussian |
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