pwc-archive/papers-with-abstracts huggingface dataset:
Supervised classification for object identification in urban areas using satellite imagery
Authors: Hazrat Ali, Adnan Ali Awan, Sanaullah Khan, Omer Shafique, Atiq ur Rahman, Shahid Khan
Date: 2018-08-02 00:00:00
Tasks: Classification, General Classification

This paper presents a useful method to achieve classification in satellite
imagery. The approach is based on pixel level study employing various features
such as correlation, homogeneity, energy and contrast. In this study gray-scale
images are used for training the classification model. For supervised
classification, two classification techniques are employed namely the Support
Vector Machine (SVM) and the Naive Bayes. With textural features used for
gray-scale images, Naive Bayes performs better with an overall accuracy of 76%
compared to 68% achieved by SVM. The computational time is evaluated while
performing the experiment with two different window sizes i.e., 50x50 and
70x70. The required computational time on a single image is found to be 27
seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition
Authors: Che-Wei Huang, Shrikanth. S. Narayanan
Date: 2018-08-02 00:00:00
Tasks: Data Augmentation, Emotion Recognition, General Classification, Speech Emotion Recognition

Regularization is crucial to the success of many practical deep learning
models, in particular in a more often than not scenario where there are only a
few to a moderate number of accessible training samples. In addition to weight
decay, data augmentation and dropout, regularization based on multi-branch
architectures, such as Shake-Shake regularization, has been proven successful
in many applications and attracted more and more attention. However, beyond
model-based representation augmentation, it is unclear how Shake-Shake
regularization helps to provide further improvement on classification tasks,
let alone the baffling interaction between batch normalization and shaking. In
this work, we present our investigation on Shake-Shake regularization, drawing
connections to the vicinal risk minimization principle and discriminative
feature learning in verification tasks. Furthermore, we identify a strong
resemblance between batch normalized residual blocks and batch normalized
recurrent neural networks, where both of them share a similar convergence
behavior, which could be mitigated by a proper initialization of batch
normalization. Based on the findings, our experiments on speech emotion
recognition demonstrate simultaneously an improvement on the classification
accuracy and a reduction on the generalization gap both with statistical
significance.
Geometry-Based Multiple Camera Head Detection in Dense Crowds
Authors: Nicola Pellicanò, Emanuel Aldea, Sylvie Le Hégarat-Mascle
Date: 2018-08-02 00:00:00
Tasks: Head Detection

This paper addresses the problem of head detection in crowded environments.
Our detection is based entirely on the geometric consistency across cameras
with overlapping fields of view, and no additional learning process is
required. We propose a fully unsupervised method for inferring scene and camera
geometry, in contrast to existing algorithms which require specific calibration
procedures. Moreover, we avoid relying on the presence of body parts other than
heads or on background subtraction, which have limited effectiveness under
heavy clutter. We cast the head detection problem as a stereo MRF-based
optimization of a dense pedestrian height map, and we introduce a constraint
which aligns the height gradient according to the vertical vanishing point
direction. We validate the method in an outdoor setting with varying pedestrian
density levels. With only three views, our approach is able to detect
simultaneously tens of heavily occluded pedestrians across a large, homogeneous
area.
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory
Authors: Jia-xin Cai, Xin Tang
Date: 2018-08-02 00:00:00
Tasks: Action Recognition, Decoder, Temporal Action Localization

Action recognition has attracted increasing attention from RGB input in
computer vision partially due to potential applications on somatic simulation
and statistics of sport such as virtual tennis game and tennis techniques and
tactics analysis by video. Recently, deep learning based methods have achieved
promising performance for action recognition. In this paper, we propose
weighted Long Short-Term Memory adopted with convolutional neural network
representations for three dimensional tennis shots recognition. First, the
local two-dimensional convolutional neural network spatial representations are
extracted from each video frame individually using a pre-trained Inception
network. Then, a weighted Long Short-Term Memory decoder is introduced to take
the output state at time t and the historical embedding feature at time t-1 to
generate feature vector using a score weighting scheme. Finally, we use the
adopted CNN and weighted LSTM to map the original visual features into a vector
space to generate the spatial-temporal semantical description of visual
sequences and classify the action video content. Experiments on the benchmark
demonstrate that our method using only simple raw RGB video can achieve better
performance than the state-of-the-art baselines for tennis shot recognition.
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
Authors: Christian Donner, Manfred Opper
Date: 2018-08-02 00:00:00
Proceeding: Journal of Machine Learning Research 2018 12
Tasks: Bayesian Inference

We present an approximate Bayesian inference approach for estimating the intensity of an inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function. Augmenting the model using a latent marked Poisson process and P\'olya--Gamma random variables we obtain a representation of the likelihood which is conjugate to the GP prior. We estimate the posterior using a variational free--form mean field optimisation together with the framework of sparse GPs. Furthermore, as alternative approximation we suggest a sparse Laplace's method for the posterior, for which an efficient expectation--maximisation algorithm is derived to find the posterior's mode. Both algorithms compare well against exact inference obtained by a Markov Chain Monte Carlo sampler and standard variational Gauss approach solving the same model, while being one order of magnitude faster. Furthermore, the performance and speed of our method is competitive with that of another recently proposed Poisson process model based on a quadratic link function, while not being limited to GPs with squared exponential kernels and rectangular domains.
Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency
Authors: Zhanyu Ma
Date: 2018-08-02 00:00:00
Tasks: Quantization

In this paper, we continue our previous work on the Dirichlet mixture model (DMM)-based VQ to derive the performance bound of the LSF VQ. The LSF parameters are transformed into the $\Delta$LSF domain and the underlying distribution of the $\Delta$LSF parameters are modelled by a DMM with finite number of mixture components. The quantization distortion, in terms of the mean squared error (MSE), is calculated with the high rate theory. The mapping relation between the perceptually motivated log spectral distortion (LSD) and the MSE is empirically approximated by a polynomial. With this mapping function, the minimum required bit rate for transparent coding of the LSF is estimated.
Classification of EEG Signal based on non-Gaussian Neutral Vector
Authors: Zhanyu Ma
Date: 2018-08-02 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, EEG Signal Classification, Electroencephalogram (EEG), feature selection, General Classification

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) representations can reveal features related to the transient nature of the EEG signals, the mDWT coefficients have been frequently used in EEG signal classification. In our previous work, we have proposed a super-Dirichlet distribution-based classifier, which utilized the nonnegative and sum-to-one properties of the mDWT coefficients. The proposed classifier performed better than the state-of-the-art support vector machine-based classifier. In this paper, we further study the neutrality of the mDWT coefficients. Assuming the mDWT vector coefficients to be a neutral vector, we transform them non-linearly into a set of independent scalar coefficients. Feature selection strategy is proposed on the transformed feature domain. Experimental results show that the feature selection strategy helps improving the classification accuracy.
Mobile big data analysis with machine learning
Authors: Jiyang Xie, Zeyu Song, Yupeng Li, Zhanyu Ma
Date: 2018-08-02 00:00:00
Tasks: BIG-bench Machine Learning, speech-recognition, Speech Recognition

This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently adopted methods of data analysis are reviewed. Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.
Weakly Supervised Localisation for Fetal Ultrasound Images
Authors: Nicolas Toussaint, Bishesh Khanal, Matthew Sinclair, Alberto Gomez, Emily Skelton, Jacqueline Matthew, Julia A. Schnabel
Date: 2018-08-02 00:00:00
Tasks: Pose Estimation, Segmentation

This paper addresses the task of detecting and localising fetal anatomical
regions in 2D ultrasound images, where only image-level labels are present at
training, i.e. without any localisation or segmentation information. We examine
the use of convolutional neural network architectures coupled with soft
proposal layers. The resulting network simultaneously performs anatomical
region detection (classification) and localisation tasks. We generate a
proposal map describing the attention of the network for a particular class.
The network is trained on 85,500 2D fetal Ultrasound images and their
associated labels. Labels correspond to six anatomical regions: head, spine,
thorax, abdomen, limbs, and placenta. Detection achieves an average accuracy of
90\% on individual regions, and show that the proposal maps correlate well with
relevant anatomical structures. This work presents itself as a powerful and
essential step towards subsequent tasks such as fetal position and pose
estimation, organ-specific segmentation, or image-guided navigation. Code and
additional material is available at https://ntoussaint.github.io/fetalnav.
The Quest for the Golden Activation Function
Authors: Mina Basirat, Peter M. Roth
Date: 2018-08-02 00:00:00
Tasks: image-classification, Image Classification

Deep Neural Networks have been shown to be beneficial for a variety of tasks,
in particular allowing for end-to-end learning and reducing the requirement for
manual design decisions. However, still many parameters have to be chosen in
advance, also raising the need to optimize them. One important, but often
ignored system parameter is the selection of a proper activation function.
Thus, in this paper we target to demonstrate the importance of activation
functions in general and show that for different tasks different activation
functions might be meaningful. To avoid the manual design or selection of
activation functions, we build on the idea of genetic algorithms to learn the
best activation function for a given task. In addition, we introduce two new
activation functions, ELiSH and HardELiSH, which can easily be incorporated in
our framework. In this way, we demonstrate for three different image
classification benchmarks that different activation functions are learned, also
showing improved results compared to typically used baselines.
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Authors: Maximilian Jaritz, Raoul de Charette, Emilie Wirbel, Xavier Perrotton, Fawzi Nashashibi
Date: 2018-08-02 00:00:00
Tasks: Depth Completion, Semantic Segmentation

Convolutional neural networks are designed for dense data, but vision data is
often sparse (stereo depth, point clouds, pen stroke, etc.). We present a
method to handle sparse depth data with optional dense RGB, and accomplish
depth completion and semantic segmentation changing only the last layer. Our
proposal efficiently learns sparse features without the need of an additional
validity mask. We show how to ensure network robustness to varying input
sparsities. Our method even works with densities as low as 0.8% (8 layer
lidar), and outperforms all published state-of-the-art on the Kitti depth
completion benchmark.
Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
Authors: Bo Yang, Sen Wang, Andrew Markham, Niki Trigoni
Date: 2018-08-02 00:00:00
Tasks: 3D Object Reconstruction, 3D Reconstruction, Multi-View 3D Reconstruction

We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches.
Online Aggregation of Unbounded Losses Using Shifting Experts with Confidence
Authors: Vladimir V'yugin, Vladimir Trunov
Date: 2018-08-02 00:00:00
Tasks: valid

We develop the setting of sequential prediction based on shifting experts and on a "smooth" version of the method of specialized experts. To aggregate experts predictions, we use the AdaHedge algorithm, which is a version of the Hedge algorithm with adaptive learning rate, and extend it by the meta-algorithm Fixed Share. Due to this, we combine the advantages of both algorithms: (1) we use the shifting regret which is a more optimal characteristic of the algorithm; (2) regret bounds are valid in the case of signed unbounded losses of the experts. Also, (3) we incorporate in this scheme a "smooth" version of the method of specialized experts which allows us to make more flexible and accurate predictions. All results are obtained in the adversarial setting -- no assumptions are made about the nature of data source. We present results of numerical experiments for short-term forecasting of electricity consumption based on a real data.
Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
Authors: Minyoung Chung, Jingyu Lee, Minkyung Lee, Jeongjin Lee, Yeong-Gil Shin
Date: 2018-08-02 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Liver Segmentation, Segmentation, Semantic Segmentation

Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
Authors: Olga Krestinskaya, Alex Pappachen James
Date: 2018-08-02 00:00:00

The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%.
Dynamic Adaptation on Non-Stationary Visual Domains
Authors: Sindi Shkodrani, Michael Hofmann, Efstratios Gavves
Date: 2018-08-02 00:00:00
Tasks: Domain Adaptation, Semantic Segmentation

Domain adaptation aims to learn models on a supervised source domain that
perform well on an unsupervised target. Prior work has examined domain
adaptation in the context of stationary domain shifts, i.e. static data sets.
However, with large-scale or dynamic data sources, data from a defined domain
is not usually available all at once. For instance, in a streaming data
scenario, dataset statistics effectively become a function of time. We
introduce a framework for adaptation over non-stationary distribution shifts
applicable to large-scale and streaming data scenarios. The model is adapted
sequentially over incoming unsupervised streaming data batches. This enables
improvements over several batches without the need for any additionally
annotated data. To demonstrate the effectiveness of our proposed framework, we
modify associative domain adaptation to work well on source and target data
batches with unequal class distributions. We apply our method to several
adaptation benchmark datasets for classification and show improved classifier
accuracy not only for the currently adapted batch, but also when applied on
future stream batches. Furthermore, we show the applicability of our
associative learning modifications to semantic segmentation, where we achieve
competitive results.
Approximate Probabilistic Neural Networks with Gated Threshold Logic
Authors: Olga Krestinskaya, Alex Pappachen James
Date: 2018-08-02 00:00:00
Tasks: General Classification, Quantization

Probabilistic Neural Network (PNN) is a feed-forward artificial neural
network developed for solving classification problems. This paper proposes a
hardware implementation of an approximated PNN (APNN) algorithm in which the
conventional exponential function of the PNN is replaced with gated threshold
logic. The weights of the PNN are approximated using a memristive crossbar
architecture. In particular, the proposed algorithm performs normalization of
the training weights, and quantization into 16 levels which significantly
reduces the complexity of the circuit.
Inlining External Sources in Answer Set Programs
Authors: Christoph Redl
Date: 2018-08-02 00:00:00

HEX-programs are an extension of answer set programs (ASP) with external
sources. To this end, external atoms provide a bidirectional interface between
the program and an external source. The traditional evaluation algorithm for
HEX-programs is based on guessing truth values of external atoms and verifying
them by explicit calls of the external source. The approach was optimized by
techniques that reduce the number of necessary verification calls or speed them
up, but the remaining external calls are still expensive. In this paper we
present an alternative evaluation approach based on inlining of external atoms,
motivated by existing but less general approaches for specialized formalisms
such as DL-programs. External atoms are then compiled away such that no
verification calls are necessary. The approach is implemented in the dlvhex
reasoner. Experiments show a significant performance gain. Besides performance
improvements, we further exploit inlining for extending previous (semantic)
characterizations of program equivalence from ASP to HEX-programs, including
those of strong equivalence, uniform equivalence and H, B -equivalence.
Finally, based on these equivalence criteria, we characterize also
inconsistency of programs wrt. extensions. Since well-known ASP extensions
(such as constraint ASP) are special cases of HEX, the results are interesting
beyond the particular formalism. Under consideration in Theory and Practice of
Logic Programming (TPLP).
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Authors: Fan Wang, Sach Mukherjee, Sylvia Richardson, Steven M. Hill
Date: 2018-08-02 00:00:00
Tasks: regression, Variable Selection

Penalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well-developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight and guidance to users. In this paper we present a large-scale comparison of penalized regression methods. We distinguish between three related goals: prediction, variable selection and variable ranking. Our results span more than 2,300 data-generating scenarios, including both synthetic and semi-synthetic data (real covariates and simulated responses), allowing us to systematically consider the influence of various factors (sample size, dimensionality, sparsity, signal strength and multicollinearity). We consider several widely-used approaches (Lasso, Adaptive Lasso, Elastic Net, Ridge Regression, SCAD, the Dantzig Selector and Stability Selection). We find considerable variation in performance between methods. Our results support a `no panacea' view, with no unambiguous winner across all scenarios or goals, even in this restricted setting where all data align well with the assumptions underlying the methods. The study allows us to make some recommendations as to which approaches may be most (or least) suitable given the goal and some data characteristics. Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.