pwc-archive/papers-with-abstracts huggingface dataset:
Generalization of k-means Related Algorithms
Authors: Yiwei Li
Date: 2019-03-24 00:00:00

This article briefly introduced Arthur and Vassilvitshii's work on
\textbf{k-means++} algorithm and further generalized the center initialization
process. It is found that choosing the most distant sample point from the
nearest center as new center can mostly have the same effect as the center
initialization process in the \textbf{k-means++} algorithm.
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Authors: Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk, Cesar Hervas-Martinez
Date: 2019-03-24 00:00:00
Tasks: Binary Classification, Data Augmentation, General Classification

Data augmentation is rapidly gaining attention in machine learning. Synthetic
data can be generated by simple transformations or through the data
distribution. In the latter case, the main challenge is to estimate the label
associated to new synthetic patterns. This paper studies the effect of
generating synthetic data by convex combination of patterns and the use of
these as unsupervised information in a semi-supervised learning framework with
support vector machines, avoiding thus the need to label synthetic examples. We
perform experiments on a total of 53 binary classification datasets. Our
results show that this type of data over-sampling supports the well-known
cluster assumption in semi-supervised learning, showing outstanding results for
small high-dimensional datasets and imbalanced learning problems.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Authors: Joel Brogan, Aparna Bharati, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer
Date: 2019-03-24 00:00:00
Tasks: Clustering, Content-Based Image Retrieval, Image Retrieval, object-detection, Object Detection, Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working onContent-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the results of a search query should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. We propose a new approach for spatial verification that aims at modeling object-level regions dynamically clustering keypoints in a 2D Hough space, which are then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations on CPUs. OS2OS performs comparably to state-of-the-art methods in classic CBIR problems, on the Oxford5K, Paris 6K, and Google-Landmarks datasets, without the need for bounding boxes. It also succeeds in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style composite imagery fromReddit.
A mixture of experts model for predicting persistent weather patterns
Authors: Maria Perez-Ortiz, Pedro A. Gutierrez, Peter Tino, Carlos Casanova-Mateo, Sancho Salcedo-Sanz
Date: 2019-03-24 00:00:00
Tasks: Mixture-of-Experts, Weather Forecasting

Weather and atmospheric patterns are often persistent. The simplest weather
forecasting method is the so-called persistence model, which assumes that the
future state of a system will be similar (or equal) to the present state.
Machine learning (ML) models are widely used in different weather forecasting
applications, but they need to be compared to the persistence model to analyse
whether they provide a competitive solution to the problem at hand. In this
paper, we devise a new model for predicting low-visibility in airports using
the concepts of mixture of experts. Visibility level is coded as two different
ordered categorical variables: cloud height and runway visual height. The
underlying system in this application is stagnant approximately in 90% of the
cases, and standard ML models fail to improve on the performance of the
persistence model. Because of this, instead of trying to simply beat the
persistence model using ML, we use this persistence as a baseline and learn an
ordinal neural network model that refines its results by focusing on learning
weather fluctuations. The results show that the proposal outperforms
persistence and other ordinal autoregressive models, especially for longer time
horizon predictions and for the runway visual height variable.
PLMP -- Point-Line Minimal Problems in Complete Multi-View Visibility
Authors: Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla
Date: 2019-03-24 00:00:00
Tasks: 3D Reconstruction, General Classification

We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras. We show that there are only 30 minimal problems in total, no problems exist for more than 6 cameras, for more than 5 points, and for more than 6 lines. We present a sequence of tests for detecting minimality starting with counting degrees of freedom and ending with full symbolic and numeric verification of representative examples. For all minimal problems discovered, we present their algebraic degrees, i.e. the number of solutions, which measure their intrinsic difficulty. It shows how exactly the difficulty of problems grows with the number of views. Importantly, several new minimal problems have small degrees that might be practical in image matching and 3D reconstruction.
Approximate Query Processing using Deep Generative Models
Authors: Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das
Date: 2019-03-24 00:00:00

Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model. The model is often compact - few hundred KBs - so that arbitrary AQP queries could be answered on the client side without contacting the database server. Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy. Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency.
Multi-Attribute Selectivity Estimation Using Deep Learning
Authors: Shohedul Hasan, Saravanan Thirumuruganathan, Jees Augustine, Nick Koudas, Gautam Das
Date: 2019-03-24 00:00:00
Tasks: Attribute, Deep Learning, Density Estimation

Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases. Accurate estimation of query selectivity involving multiple correlated attributes is especially challenging. Poor cardinality estimates could result in the selection of bad plans by the query optimizer. We investigate the feasibility of using deep learning based approaches for both point and range queries and propose two complementary approaches. Our first approach considers selectivity as an unsupervised deep density estimation problem. We successfully introduce techniques from neural density estimation for this purpose. The key idea is to decompose the joint distribution into a set of tractable conditional probability distributions such that they satisfy the autoregressive property. Our second approach formulates selectivity estimation as a supervised deep learning problem that predicts the selectivity of a given query. We also introduce and address a number of practical challenges arising when adapting deep learning for relational data. These include query/data featurization, incorporating query workload information in a deep learning framework and the dynamic scenario where both data and workload queries could be updated. Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that our proposed techniques provide fast and accurate selective estimates with minimal space overhead.
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Authors: Zhijie Deng, Yucen Luo, Jun Zhu
Date: 2019-03-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Domain Adaptation, Unsupervised Domain Adaptation

Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution. However, such methods typically learn a domain-invariant representation space to match the marginal distributions of the source and target domains, while ignoring their fine-level structures. In this paper, we propose Cluster Alignment with a Teacher (CAT) for unsupervised domain adaptation, which can effectively incorporate the discriminative clustering structures in both domains for better adaptation. Technically, CAT leverages an implicit ensembling teacher model to reliably discover the class-conditional structure in the feature space for the unlabeled target domain. Then CAT forces the features of both the source and the target domains to form discriminative class-conditional clusters and aligns the corresponding clusters across domains. Empirical results demonstrate that CAT achieves state-of-the-art results in several unsupervised domain adaptation scenarios.
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections
Authors: Xian Wei, Hao Shen, Yuanxiang Li, Xuan Tang, Bo Jin, Lijun Zhao, Yi Lu Murphey
Date: 2019-03-24 00:00:00
Tasks: Dictionary Learning, Image Classification

There are some inadequacies in the language description of this paper that
require further improvement. This paper is based on a revision of a conference
paper. It is now necessary to further explain the difference between the
contributions of the two papers.
MUSCO: Multi-Stage Compression of neural networks
Authors: Julia Gusak, Maksym Kholiavchenko, Evgeny Ponomarev, Larisa Markeeva, Ivan Oseledets, Andrzej Cichocki
Date: 2019-03-24 00:00:00
Tasks: Neural Network Compression

The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our approach improves the compression rate while maintaining the accuracy for a variety of tasks.
Optimization of Speaker Extraction Neural Network with Magnitude and Temporal Spectrum Approximation Loss
Authors: Cheng-Lin Xu, Wei Rao, Eng Siong Chng, Haizhou Li
Date: 2019-03-24 00:00:00

The SpeakerBeam-FE (SBF) method is proposed for speaker extraction. It
attempts to overcome the problem of unknown number of speakers in an audio
recording during source separation. The mask approximation loss of SBF is
sub-optimal, which doesn't calculate direct signal reconstruction error and
consider the speech context. To address these problems, this paper proposes a
magnitude and temporal spectrum approximation loss to estimate a phase
sensitive mask for the target speaker with the speaker characteristics.
Moreover, this paper explores a concatenation framework instead of the context
adaptive deep neural network in the SBF method to encode a speaker embedding
into the mask estimation network. Experimental results under open evaluation
condition show that the proposed method achieves 70.4% and 17.7% relative
improvement over the SBF baseline on signal-to-distortion ratio (SDR) and
perceptual evaluation of speech quality (PESQ), respectively. A further
analysis demonstrates 69.1% and 72.3% relative SDR improvements obtained by the
proposed method for different and same gender mixtures.
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Authors: Ye Xia, Jinkyu Kim, John Canny, Karl Zipser, David Whitney
Date: 2019-03-24 00:00:00

Inspired by human vision, we propose a new periphery-fovea multi-resolution
driving model that predicts vehicle speed from dash camera videos. The
peripheral vision module of the model processes the full video frames in low
resolution. Its foveal vision module selects sub-regions and uses
high-resolution input from those regions to improve its driving performance. We
train the fovea selection module with supervision from driver gaze. We show
that adding high-resolution input from predicted human driver gaze locations
significantly improves the driving accuracy of the model. Our periphery-fovea
multi-resolution model outperforms a uni-resolution periphery-only model that
has the same amount of floating-point operations. More importantly, we
demonstrate that our driving model achieves a significantly higher performance
gain in pedestrian-involved critical situations than in other non-critical
situations.
Deep recommender engine based on efficient product embeddings neural pipeline
Authors: Laurentiu Piciu, Andrei Damian, Nicolae Tapus, Andrei Simion-Constantinescu, Bogdan Dumitrescu
Date: 2019-03-24 00:00:00
Tasks: Benchmarking

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper, we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid model pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking processes have been done using pharma industry retail real-life transactional Big-Data streams.
Relation extraction between the clinical entities based on the shortest dependency path based LSTM
Authors: Dhanachandra Ningthoujam, Shweta Yadav, Pushpak Bhattacharyya, Asif Ekbal
Date: 2019-03-24 00:00:00
Tasks: Relation, Relation Extraction, Sentence

Owing to the exponential rise in the electronic medical records, information
extraction in this domain is becoming an important area of research in recent
years. Relation extraction between the medical concepts such as medical
problem, treatment, and test etc. is also one of the most important tasks in
this area. In this paper, we present an efficient relation extraction system
based on the shortest dependency path (SDP) generated from the dependency
parsed tree of the sentence. Instead of relying on many handcrafted features
and the whole sequence of tokens present in a sentence, our system relies only
on the SDP between the target entities. For every pair of entities, the system
takes only the words in the SDP, their dependency labels, Part-of-Speech
information and the types of the entities as the input. We develop a dependency
parser for extracting dependency information. We perform our experiments on the
benchmark i2b2 dataset for clinical relation extraction challenge 2010.
Experimental results show that our system outperforms the existing systems.
Variational Inference with Latent Space Quantization for Adversarial Resilience
Authors: Vinay Kyatham, Mayank Mishra, Tarun Kumar Yadav, Deepak Mishra, Prathosh AP
Date: 2019-03-24 00:00:00
Tasks: Quantization, valid, Variational Inference

Despite their tremendous success in modelling high-dimensional data manifolds, deep neural networks suffer from the threat of adversarial attacks - Existence of perceptually valid input-like samples obtained through careful perturbation that lead to degradation in the performance of the underlying model. Major concerns with existing defense mechanisms include non-generalizability across different attacks, models and large inference time. In this paper, we propose a generalized defense mechanism capitalizing on the expressive power of regularized latent space based generative models. We design an adversarial filter, devoid of access to classifier and adversaries, which makes it usable in tandem with any classifier. The basic idea is to learn a Lipschitz constrained mapping from the data manifold, incorporating adversarial perturbations, to a quantized latent space and re-map it to the true data manifold. Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference. We demonstrate the efficacy of the proposed formulation in providing resilience against multiple attack types (black and white box) and methods, while being almost real-time. Our experiments show that the proposed method surpasses the state-of-the-art techniques in several cases.
KPTransfer: improved performance and faster convergence from keypoint subset-wise domain transfer in human pose estimation
Authors: Kanav Vats, Helmut Neher, Alexander Wong, David A. Clausi, John Zelek
Date: 2019-03-24 00:00:00
Tasks: Keypoint Detection, Pose Estimation

In this paper, we present a novel approach called KPTransfer for improving
modeling performance for keypoint detection deep neural networks via domain
transfer between different keypoint subsets. This approach is motivated by the
notion that rich contextual knowledge can be transferred between different
keypoint subsets representing separate domains. In particular, the proposed
method takes into account various keypoint subsets/domains by sequentially
adding and removing keypoints. Contextual knowledge is transferred between two
separate domains via domain transfer. Experiments to demonstrate the efficacy
of the proposed KPTransfer approach were performed for the task of human pose
estimation on the MPII dataset, with comparisons against random initialization
and frozen weight extraction configurations. Experimental results demonstrate
the efficacy of performing domain transfer between two different joint subsets
resulting in a PCKh improvement of up to 1.1 over random initialization on
joints such as wrists and knee in certain joint splits with an overall PCKh
improvement of 0.5. Domain transfer from a different set of joints not only
results in improved accuracy but also results in faster convergence because of
mutual co-adaptations of weights resulting from the contextual knowledge of the
pose from a different set of joints.
SRGAN: Training Dataset Matters
Authors: Nao Takano, Gita Alaghband
Date: 2019-03-24 00:00:00

Generative Adversarial Networks (GANs) in supervised settings can generate
photo-realistic corresponding output from low-definition input (SRGAN). Using
the architecture presented in the SRGAN original paper [2], we explore how
selecting a dataset affects the outcome by using three different datasets to
see that SRGAN fundamentally learns objects, with their shape, color, and
texture, and redraws them in the output rather than merely attempting to
sharpen edges. This is further underscored with our demonstration that once the
network learns the images of the dataset, it can generate a photo-like image
with even a slight hint of what it might look like for the original from a very
blurry edged sketch. Given a set of inference images, the network trained with
the same dataset results in a better outcome over the one trained with
arbitrary set of images, and we report its significance numerically with
Frechet Inception Distance score [22].
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework
Authors: Lamei Zhang, Hongwei Dong, Bin Zou
Date: 2019-03-24 00:00:00
Tasks: Classification, Form, General Classification, image-classification, Image Classification, Representation Learning

Convolutional neural networks (CNNs) have been widely used to improve the accuracy of polarimetric synthetic aperture radar (PolSAR) image classification. However, in most studies, the difference between PolSAR images and optical images is rarely considered. Most of the existing CNNs are not tailored for the task of PolSAR image classification, in which complex-valued PolSAR data have been simply equated to real-valued data to fit the optical image processing architectures and avoid complex-valued operations. This is one of the reasons CNNs unable to perform their full capabilities in PolSAR classification. To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form. In this paper, considering the properties of complex-valued numbers, amplitude and phase of complex-valued PolSAR data are extracted as the input for the first time to maintain the integrity of original information while avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN) architecture is proposed to match the improved input form and achieve better classification results. Furthermore, depthwise separable convolution is introduced to the proposed architecture in order to better extract information from the phase information. Experiments on three PolSAR benchmark datasets not only prove that using amplitude and phase as the input do contribute to the improvement of PolSAR classification, but also verify the adaptability between the improved input form and the well-designed architectures.