pwc-archive/papers-with-abstracts huggingface dataset:
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Authors: Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
Date: 2019-02-11 00:00:00
Proceeding: ICLR 2020 1
Tasks: Bayesian Inference, Deep Learning, Stochastic Optimization

The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We also prove non-asymptotic convergence of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the scalability and effectiveness of cyclical SG-MCMC in learning complex multimodal distributions, especially for fully Bayesian inference with modern deep neural networks.
Reconstructing dynamical networks via feature ranking
Authors: Marc G. Leguia, Zoran Levnajic, Ljupco Todorovski, Bernard Zenko
Date: 2019-02-11 00:00:00

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
Authors: Ralph Gasser, Luca Rossetto, Heiko Schuldt
Date: 2019-02-11 00:00:00
Tasks: All, Information Retrieval, Management, Retrieval

The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system.
Deep Hashing using Entropy Regularised Product Quantisation Network
Authors: Jo Schlemper, Jose Caballero, Andy Aitken, Joost van Amersfoort
Date: 2019-02-11 00:00:00
Tasks: Deep Hashing

In large scale systems, approximate nearest neighbour search is a crucial
algorithm to enable efficient data retrievals. Recently, deep learning-based
hashing algorithms have been proposed as a promising paradigm to enable data
dependent schemes. Often their efficacy is only demonstrated on data sets with
fixed, limited numbers of classes. In practical scenarios, those labels are not
always available or one requires a method that can handle a higher input
variability, as well as a higher granularity. To fulfil those requirements, we
look at more flexible similarity measures. In this work, we present a novel,
flexible, end-to-end trainable network for large-scale data hashing. Our method
works by transforming the data distribution to behave as a uniform distribution
on a product of spheres. The transformed data is subsequently hashed to a
binary form in a way that maximises entropy of the output, (i.e. to fully
utilise the available bit-rate capacity) while maintaining the correctness
(i.e. close items hash to the same key in the map). We show that the method
outperforms baseline approaches such as locality-sensitive hashing and product
quantisation in the limited capacity regime.
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
Authors: Yashar Kiarashinejad, Sajjad Abdollahramezani, Ali Adibi
Date: 2019-02-11 00:00:00
Tasks: Dimensionality Reduction

In this paper, we demonstrate a computationally efficient new approach based
on deep learning (DL) techniques for analysis, design, and optimization of
electromagnetic (EM) nanostructures. We use the strong correlation among
features of a generic EM problem to considerably reduce the dimensionality of
the problem and thus, the computational complexity, without imposing
considerable errors. By employing the dimensionality reduction concept using
the more recently demonstrated autoencoder technique, we redefine the
conventional many-to-one design problem in EM nanostructures into a one-to-one
problem plus a much simpler many-to-one problem, which can be simply solved
using an analytic formulation. This approach reduces the computational
complexity in solving both the forward problem (i.e., analysis) and the inverse
problem (i.e., design) by orders of magnitude compared to conventional
approaches. In addition, it provides analytic formulations that, despite their
complexity, can be used to obtain intuitive understanding of the physics and
dynamics of EM wave interaction with nanostructures with minimal computation
requirements. As a proof-of-concept, we applied such an efficacious method to
design a new class of on-demand reconfigurable optical metasurfaces based on
phase-change materials (PCM). We envision that the integration of such a
DL-based technique with full-wave commercial software packages offers a
powerful toolkit to facilitate the analysis, design, and optimization of the EM
nanostructures as well as explaining, understanding, and predicting the
observed responses in such structures.
Global Collaboration through Local Interaction in Competitive Learning
Authors: Abbas Siddiqui, Dionysios Georgiadis
Date: 2019-02-11 00:00:00

Feature maps, that preserve the global topology of arbitrary datasets, can be
formed by self-organizing competing agents. So far, it has been presumed that
global interaction of agents is necessary for this process. We establish that
this is not the case, and that global topology can be uncovered through
strictly local interactions. Enforcing uniformity of map quality across all
agents, results in an algorithm that is able to consistently uncover the global
topology of diversely challenging datasets.The applicability and scalability of
this approach is further tested on a large point cloud dataset, revealing a
linear relation between map training time and size. The presented work not only
reduces algorithmic complexity but also constitutes first step towards a
distributed self organizing map.
Robust statistics and no-reference image quality assessment in Curvelet domain
Authors: Ramon Giostri Campos, Evandro Ottoni Teatini Salles
Date: 2019-02-11 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

This paper uses robust statistics and curvelet transform to learn a
general-purpose no-reference (NR) image quality assessment (IQA) model. The new
approach, here called M1, competes with the Curvelet Quality Assessment
proposed in 2014 (Curvelet2014). The central idea is to use descriptors based
on robust statistics to extract features and predict the human opinion about
degraded images. To show the consistency of the method the model is tested with
3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it is
used the Wilcoxon test to verify the statistical significance of results and
promote an accurate comparison between new model M1 and Curvelet2014. The
results show a gain when robust statistics are used as descriptor.
A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering
Authors: Gaël Beck, Tarn Duong, Mustapha Lebbah, Hanane Azzag, Christophe Cérin
Date: 2019-02-11 00:00:00
Tasks: Clustering

In this paper we target the class of modal clustering methods where clusters
are defined in terms of the local modes of the probability density function
which generates the data. The most well-known modal clustering method is the
k-means clustering. Mean Shift clustering is a generalization of the k-means
clustering which computes arbitrarily shaped clusters as defined as the basins
of attraction to the local modes created by the density gradient ascent paths.
Despite its potential, the Mean Shift approach is a computationally expensive
method for unsupervised learning. Thus, we introduce two contributions aiming
to provide clustering algorithms with a linear time complexity, as opposed to
the quadratic time complexity for the exact Mean Shift clustering. Firstly we
propose a scalable procedure to approximate the density gradient ascent.
Second, our proposed scalable cluster labeling technique is presented. Both
propositions are based on Locality Sensitive Hashing (LSH) to approximate
nearest neighbors. These two techniques may be used for moderate sized
datasets. Furthermore, we show that using our proposed approximations of the
density gradient ascent as a pre-processing step in other clustering methods
can also improve dedicated classification metrics. For the latter, a
distributed implementation, written for the Spark/Scala ecosystem is proposed.
For all these considered clustering methods, we present experimental results
illustrating their labeling accuracy and their potential to solve concrete
problems.
Semantic Hierarchical Priors for Intrinsic Image Decomposition
Authors: Saurabh Saini, P. J. Narayanan
Date: 2019-02-11 00:00:00
Tasks: Intrinsic Image Decomposition

Intrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several fields. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scene properties at each pixel within a small neighbourhood. Mid-level context priors encode object level semantics. Global context priors establish correspondences at the scene level. Our semantic priors are designed on both fixed and flexible regions, using selective search method and Convolutional Neural Network features. Our IID method is an iterative multistage optimization scheme and consists of two complementary formulations: $L_2$ smoothing for shading and $L_1$ sparsity for reflectance. Experiments and analysis of our method indicate the utility of our semantic priors and structured hierarchical analysis in an IID framework. We compare our method with other contemporary IID solutions and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results even when compared to end-to-end deep learning IID methods, signifying the importance of such priors. We believe that the insights and techniques presented in this paper would be useful in the future IID research.
Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications
Authors: Vivek Kumar, Brojo Kishore Mishra, Manuel Mazzara, Dang N. H. Thanh, Abhishek Verma
Date: 2019-02-11 00:00:00
Tasks: Attribute, Cancer Classification, Classification, General Classification

As much as data science is playing a pivotal role everywhere, healthcare also
finds it prominent application. Breast Cancer is the top rated type of cancer
amongst women; which took away 627,000 lives alone. This high mortality rate
due to breast cancer does need attention, for early detection so that
prevention can be done in time. As a potential contributor to state-of-art
technology development, data mining finds a multi-fold application in
predicting Brest cancer. This work focuses on different classification
techniques implementation for data mining in predicting malignant and benign
breast cancer. Breast Cancer Wisconsin data set from the UCI repository has
been used as experimental dataset while attribute clump thickness being used as
an evaluation class. The performances of these twelve algorithms: Ada Boost M
1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass
Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree
are analyzed on this data set. Keywords- Data Mining, Classification
Techniques, UCI repository, Breast Cancer, Classification Algorithms
GET-AID: Visual Recognition of Human Rights Abuses via Global Emotional Traits
Authors: Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus D. McDonald-Maier
Date: 2019-02-11 00:00:00

In the era of social media and big data, the use of visual evidence to
document conflict and human rights abuse has become an important element for
human rights organizations and advocates. In this paper, we address the task of
detecting two types of human rights abuses in challenging, everyday photos: (1)
child labour, and (2) displaced populations. We propose a novel model that is
driven by a human-centric approach. Our hypothesis is that the emotional state
of a person -- how positive or pleasant an emotion is, and the control level of
the situation by the person -- are powerful cues for perceiving potential human
rights violations. To exploit these cues, our model learns to predict global
emotional traits over a given image based on the joint analysis of every
detected person and the whole scene. By integrating these predictions with a
data-driven convolutional neural network (CNN) classifier, our system
efficiently infers potential human rights abuses in a clean, end-to-end system
we call GET-AID (from Global Emotional Traits for Abuse IDentification).
Extensive experiments are performed to verify our method on the recently
introduced subset of Human Rights Archive (HRA) dataset (2 violation categories
with the same number of positive and negative samples), where we show
quantitatively compelling results. Compared with previous works and the sole
use of a CNN classifier, this paper improves the coverage up to 23.73% for
child labour and 57.21% for displaced populations. Our dataset, codes and
trained models are available online at https://github.com/GKalliatakis/GET-AID.
An Online Sample Based Method for Mode Estimation using ODE Analysis of Stochastic Approximation Algorithms
Authors: Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Prabuchandran K. J., Shalabh Bhatnagar
Date: 2019-02-11 00:00:00

One of the popular measures of central tendency that provides better representation and interesting insights of the data compared to the other measures like mean and median is the metric mode. If the analytical form of the density function is known, mode is an argument of the maximum value of the density function and one can apply the optimization techniques to find mode. In many of the practical applications, the analytical form of the density is not known and only the samples from the distribution are available. Most of the techniques proposed in the literature for estimating the mode from the samples assume that all the samples are available beforehand. Moreover, some of the techniques employ computationally expensive operations like sorting. In this work we provide a computationally effective, on-line iterative algorithm that estimates the mode of a unimodal smooth density given only the samples generated from the density. Asymptotic convergence of the proposed algorithm using an ordinary differential equation (ODE) based analysis is provided. We also prove the stability of estimates by utilizing the concept of regularization. Experimental results further demonstrate the effectiveness of the proposed algorithm.
Additional Baseline Metrics for the paper "Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification"
Authors: Claudio Ferrari, Stefano Berretti, Alberto del Bimbo
Date: 2019-02-11 00:00:00
Tasks: Face Identification

In this report, we provide additional and corrected results for the paper
"Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face
Identification". After further investigations, we discovered and corrected
wrongly labeled images and incorrect identities. This forced us to re-generate
the evaluation protocol for the new data; in doing so, we also reproduced and
extended the experimental results with other standard metrics and measures used
in the literature. The reader can refer to the original paper for additional
details regarding the data collection procedure and recognition pipeline.
Exploiting Structure of Uncertainty for Efficient Matroid Semi-Bandits
Authors: Pierre Perrault, Vianney Perchet, Michal Valko
Date: 2019-02-11 00:00:00

We improve the efficiency of algorithms for stochastic \emph{combinatorial semi-bandits}. In most interesting problems, state-of-the-art algorithms take advantage of structural properties of rewards, such as \emph{independence}. However, while being optimal in terms of asymptotic regret, these algorithms are inefficient. In our paper, we first reduce their implementation to a specific \emph{submodular maximization}. Then, in case of \emph{matroid} constraints, we design adapted approximation routines, thereby providing the first efficient algorithms that rely on reward structure to improve regret bound. In particular, we improve the state-of-the-art efficient gap-free regret bound by a factor $\sqrt{m}/\log m$, where $m$ is the maximum action size. Finally, we show how our improvement translates to more general \emph{budgeted combinatorial semi-bandits}.
Understanding over-parameterized deep networks by geometrization
Authors: Xiao Dong, Ling Zhou
Date: 2019-02-11 00:00:00
Tasks: Sensitivity

A complete understanding of the widely used over-parameterized deep networks
is a key step for AI. In this work we try to give a geometric picture of
over-parameterized deep networks using our geometrization scheme. We show that
the Riemannian geometry of network complexity plays a key role in understanding
the basic properties of over-parameterizaed deep networks, including the
generalization, convergence and parameter sensitivity. We also point out deep
networks share lots of similarities with quantum computation systems. This can
be regarded as a strong support of our proposal that geometrization is not only
the bible for physics, it is also the key idea to understand deep learning
systems.
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
Authors: Suryansh Kumar, Ram Srivatsav Ghorakavi, Yuchao Dai, Hongdong Li
Date: 2019-02-11 00:00:00
Tasks: Depth Estimation, Motion Estimation, Optical Flow Estimation

Recent geometric methods need reliable estimates of 3D motion parameters to
procure accurate dense depth map of a complex dynamic scene from monocular
images \cite{kumar2017monocular, ranftl2016dense}. Generally, to estimate
\textbf{precise} measurements of relative 3D motion parameters and to validate
its accuracy using image data is a challenging task. In this work, we propose
an alternative approach that circumvents the 3D motion estimation requirement
to obtain a dense depth map of a dynamic scene. Given per-pixel optical flow
correspondences between two consecutive frames and, the sparse depth prior for
the reference frame, we show that, we can effectively recover the dense depth
map for the successive frames without solving for 3D motion parameters. Our
method assumes a piece-wise planar model of a dynamic scene, which undergoes
rigid transformation locally, and as-rigid-as-possible transformation globally
between two successive frames. Under our assumption, we can avoid the explicit
estimation of 3D rotation and translation to estimate scene depth. In essence,
our formulation provides an unconventional way to think and recover the dense
depth map of a complex dynamic scene which is incremental and motion free in
nature. Our proposed method does not make object level or any other high-level
prior assumption about the dynamic scene, as a result, it is applicable to a
wide range of scenarios. Experimental results on the benchmarks dataset show
the competence of our approach for multiple frames.
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation
Authors: Jianxin Lin, Zhibo Chen, Yingce Xia, Sen Liu, Tao Qin, Jiebo Luo
Date: 2019-02-11 00:00:00
Tasks: Attribute, Disentanglement, Image-to-Image Translation, Translation

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs). However, existing approaches are mostly designed in an unsupervised manner while little attention has been paid to domain information within unpaired data. In this paper, we treat domain information as explicit supervision and design an unpaired image-to-image translation framework, Domain-supervised GAN (DosGAN), which takes the first step towards the exploration of explicit domain supervision. In contrast to representing domain characteristics using different generators or domain codes, we pre-train a classification network to explicitly classify the domain of an image. After pre-training, this network is used to extract the domain-specific features of each image. Such features, together with the domain-independent features extracted by another encoder (shared across different domains), are used to generate image in target domain. Extensive experiments on multiple facial attribute translation, multiple identity translation, multiple season translation and conditional edges-to-shoes/handbags demonstrate the effectiveness of our method. In addition, we can transfer the domain-specific feature extractor obtained on the Facescrub dataset with domain supervision information to unseen domains, such as faces in the CelebA dataset. We also succeed in achieving conditional translation with any two images in CelebA, while previous models like StarGAN cannot handle this task.
Semantic Label Reduction Techniques for Autonomous Driving
Authors: Qadeer Khan, Torsten Schön, Patrick Wenzel
Date: 2019-02-11 00:00:00
Tasks: Autonomous Driving, Semantic Segmentation

Semantic segmentation maps can be used as input to models for maneuvering the
controls of a car. However, not all labels may be necessary for making the
control decision. One would expect that certain labels such as road lanes or
sidewalks would be more critical in comparison with labels for vegetation or
buildings which may not have a direct influence on the car's driving decision.
In this appendix, we evaluate and quantify how sensitive and important the
different semantic labels are for controlling the car. Labels that do not
influence the driving decision are remapped to other classes, thereby
simplifying the task by reducing to only labels critical for driving of the
vehicle.