pwc-archive/papers-with-abstracts huggingface dataset:
Sharp Bounds for Generalized Uniformity Testing
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Date: 2017-09-07 00:00:00
Proceeding: NeurIPS 2018 12

We study the problem of generalized uniformity testing \cite{BC17} of a
discrete probability distribution: Given samples from a probability
distribution $p$ over an {\em unknown} discrete domain $\mathbf{\Omega}$, we
want to distinguish, with probability at least $2/3$, between the case that $p$
is uniform on some {\em subset} of $\mathbf{\Omega}$ versus $\epsilon$-far, in
total variation distance, from any such uniform distribution.
  We establish tight bounds on the sample complexity of generalized uniformity
testing. In more detail, we present a computationally efficient tester whose
sample complexity is optimal, up to constant factors, and a matching
information-theoretic lower bound. Specifically, we show that the sample
complexity of generalized uniformity testing is
$\Theta\left(1/(\epsilon^{4/3}\|p\|_3) + 1/(\epsilon^{2} \|p\|_2) \right)$.
Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification
Authors: Daniel Michelsanti, Zheng-Hua Tan
Date: 2017-09-06 00:00:00
Tasks: Speaker Verification, Speech Enhancement

Improving speech system performance in noisy environments remains a
challenging task, and speech enhancement (SE) is one of the effective
techniques to solve the problem. Motivated by the promising results of
generative adversarial networks (GANs) in a variety of image processing tasks,
we explore the potential of conditional GANs (cGANs) for SE, and in particular,
we make use of the image processing framework proposed by Isola et al. [1] to
learn a mapping from the spectrogram of noisy speech to an enhanced
counterpart. The SE cGAN consists of two networks, trained in an adversarial
manner: a generator that tries to enhance the input noisy spectrogram, and a
discriminator that tries to distinguish between enhanced spectrograms provided
by the generator and clean ones from the database using the noisy spectrogram
as a condition. We evaluate the performance of the cGAN method in terms of
perceptual evaluation of speech quality (PESQ), short-time objective
intelligibility (STOI), and equal error rate (EER) of speaker verification (an
example application). Experimental results show that the cGAN method overall
outperforms the classical short-time spectral amplitude minimum mean square
error (STSA-MMSE) SE algorithm, and is comparable to a deep neural
network-based SE approach (DNN-SE).
An unsupervised long short-term memory neural network for event detection in cell videos
Authors: Ha Tran Hong Phan, Ashnil Kumar, David Feng, Michael Fulham, Jinman Kim
Date: 2017-09-07 00:00:00
Tasks: Event Detection

We propose an automatic unsupervised cell event detection and classification
method, which expands convolutional Long Short-Term Memory (LSTM) neural
networks, for cellular events in cell video sequences. Cells in images that are
captured from various biomedical applications usually have different shapes and
motility, which pose difficulties for the automated event detection in cell
videos. Current methods to detect cellular events are based on supervised
machine learning and rely on tedious manual annotation from investigators with
specific expertise. So that our LSTM network could be trained in an
unsupervised manner, we designed it with a branched structure where one branch
learns the frequent, regular appearance and movements of objects and the second
learns the stochastic events, which occur rarely and without warning in a cell
video sequence. We tested our network on a publicly available dataset of
densely packed stem cell phase-contrast microscopy images undergoing cell
division. This dataset is considered to be more challenging that a dataset with
sparse cells. We compared our method to several published supervised methods
evaluated on the same dataset and to a supervised LSTM method with a similar
design and configuration to our unsupervised method. We used an F1-score, which
is a balanced measure for both precision and recall. Our results show that our
unsupervised method has a higher or similar F1-score when compared to two fully
supervised methods that are based on Hidden Conditional Random Fields (HCRF),
and has comparable accuracy with the current best supervised HCRF-based method.
Our method was generalizable as after being trained on one video it could be
applied to videos where the cells were in different conditions. The accuracy of
our unsupervised method approached that of its supervised counterpart.
Composition by Conversation
Authors: Donya Quick, Clayton T. Morrison
Date: 2017-09-07 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Most musical programming languages are developed purely for coding virtual
instruments or algorithmic compositions. Although there has been some work in
the domain of musical query languages for music information retrieval, there
has been little attempt to unify the principles of musical programming and
query languages with cognitive and natural language processing models that
would facilitate the activity of composition by conversation. We present a
prototype framework, called MusECI, that merges these domains, permitting
score-level algorithmic composition in a text editor while also supporting
connectivity to existing natural language processing frameworks.
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Authors: Lin Wu, Yang Wang, Junbin Gao, Xue Li
Date: 2017-06-10 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.
The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis
Authors: Audrey Chung, Mohammad Javad Shafiee, Paul Fieguth, Alexander Wong
Date: 2017-09-07 00:00:00

Evolutionary deep intelligence was recently proposed as a method for
achieving highly efficient deep neural network architectures over successive
generations. Drawing inspiration from nature, we propose the incorporation of
sexual evolutionary synthesis. Rather than the current asexual synthesis of
networks, we aim to produce more compact feature representations by
synthesizing more diverse and generalizable offspring networks in subsequent
generations via the combination of two parent networks. Experimental results
were obtained using the MNIST and CIFAR-10 datasets, and showed improved
architectural efficiency and comparable testing accuracy relative to the
baseline asexual evolutionary neural networks. In particular, the network
synthesized via sexual evolutionary synthesis for MNIST had approximately
double the architectural efficiency (cluster efficiency of 34.29X and synaptic
efficiency of 258.37X) in comparison to the network synthesized via asexual
evolutionary synthesis, with both networks achieving a testing accuracy of
~97%.
Boosting the kernelized shapelets: Theory and algorithms for local features
Authors: Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai, Akiko Takeda
Date: 2017-09-05 00:00:00
Tasks: Binary Classification, General Classification, Generalization Bounds, Time Series, Time Series Analysis, Time Series Classification

We consider binary classification problems using local features of objects.
One of motivating applications is time-series classification, where features
reflecting some local closeness measure between a time series and a pattern
sequence called shapelet are useful. Despite the empirical success of such
approaches using local features, the generalization ability of resulting
hypotheses is not fully understood and previous work relies on a bunch of
heuristics. In this paper, we formulate a class of hypotheses using local
features, where the richness of features is controlled by kernels. We derive
generalization bounds of sparse ensembles over the class which is exponentially
better than a standard analysis in terms of the number of possible local
features. The resulting optimization problem is well suited to the boosting
approach and the weak learning problem is formulated as a DC program, for which
practical algorithms exist. In preliminary experiments on time-series data
sets, our method achieves competitive accuracy with the state-of-the-art
algorithms with small parameter-tuning cost.
Capturing natural-colour 3D models of insects for species discovery
Authors: Chuong V. Nguyen, David R. Lovell, Matt Adcock, John La Salle
Date: 2017-09-07 00:00:00

Collections of biological specimens are fundamental to scientific
understanding and characterization of natural diversity. This paper presents a
system for liberating useful information from physical collections by bringing
specimens into the digital domain so they can be more readily shared, analyzed,
annotated and compared. It focuses on insects and is strongly motivated by the
desire to accelerate and augment current practices in insect taxonomy which
predominantly use text, 2D diagrams and images to describe and characterize
species. While these traditional kinds of descriptions are informative and
useful, they cannot cover insect specimens "from all angles" and precious
specimens are still exchanged between researchers and collections for this
reason. Furthermore, insects can be complex in structure and pose many
challenges to computer vision systems. We present a new prototype for a
practical, cost-effective system of off-the-shelf components to acquire
natural-colour 3D models of insects from around 3mm to 30mm in length. Colour
images are captured from different angles and focal depths using a digital
single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images
are processed into 3D reconstructions using software based on a visual hull
algorithm. The resulting models are compact (around 10 megabytes), afford
excellent optical resolution, and can be readily embedded into documents and
web pages, as well as viewed on mobile devices. The system is portable, safe,
relatively affordable, and complements the sort of volumetric data that can be
acquired by computed tomography. This system provides a new way to augment the
description and documentation of insect species holotypes, reducing the need to
handle or ship specimens. It opens up new opportunities to collect data for
research, education, art, entertainment, biodiversity assessment and
biosecurity control.
Dynamic Computational Time for Visual Attention
Authors: Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu
Date: 2017-03-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a dynamic computational time model to accelerate the average
processing time for recurrent visual attention (RAM). Rather than attention
with a fixed number of steps for each input image, the model learns to decide
when to stop on the fly. To achieve this, we add an additional continue/stop
action per time step to RAM and use reinforcement learning to learn both the
optimal attention policy and stopping policy. The modification is simple but
could dramatically save the average computational time while keeping the same
recognition performance as RAM. Experimental results on CUB-200-2011 and
Stanford Cars dataset demonstrate the dynamic computational model can work
effectively for fine-grained image recognition.The source code of this paper
can be obtained from https://github.com/baidu-research/DT-RAM
Towards high-throughput 3D insect capture for species discovery and diagnostics
Authors: Chuong Nguyen, Matt Adcock, Stuart Anderson, David Lovell, Nicole Fisher, John La Salle
Date: 2017-09-07 00:00:00
Tasks: Diversity, Mixed Reality, Vocal Bursts Intensity Prediction

Digitisation of natural history collections not only preserves precious
information about biological diversity, it also enables us to share, analyse,
annotate and compare specimens to gain new insights. High-resolution,
full-colour 3D capture of biological specimens yields color and geometry
information complementary to other techniques (e.g., 2D capture, electron
scanning and micro computed tomography). However 3D colour capture of small
specimens is slow for reasons including specimen handling, the narrow depth of
field of high magnification optics, and the large number of images required to
resolve complex shapes of specimens. In this paper, we outline techniques to
accelerate 3D image capture, including using a desktop robotic arm to automate
the insect handling process; using a calibrated pan-tilt rig to avoid attaching
calibration targets to specimens; using light field cameras to capture images
at an extended depth of field in one shot; and using 3D Web and mixed reality
tools to facilitate the annotation, distribution and visualisation of 3D
digital models.
Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)
Authors: Ronald Salloum, Yuzhuo Ren, C. -C. Jay Kuo
Date: 2017-09-06 00:00:00
Tasks: Multi-Task Learning

In this work, we propose a technique that utilizes a fully convolutional
network (FCN) to localize image splicing attacks. We first evaluated a
single-task FCN (SFCN) trained only on the surface label. Although the SFCN is
shown to provide superior performance over existing methods, it still provides
a coarse localization output in certain cases. Therefore, we propose the use of
a multi-task FCN (MFCN) that utilizes two output branches for multi-task
learning. One branch is used to learn the surface label, while the other branch
is used to learn the edge or boundary of the spliced region. We trained the
networks using the CASIA v2.0 dataset, and tested the trained models on the
CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble
Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform
existing splicing localization algorithms, and that the MFCN can achieve finer
localization than the SFCN.
See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content
Authors: Roozbeh Mottaghi, Connor Schenck, Dieter Fox, Ali Farhadi
Date: 2017-01-10 00:00:00
Proceeding: ICCV 2017 10

Humans have rich understanding of liquid containers and their contents; for
example, we can effortlessly pour water from a pitcher to a cup. Doing so
requires estimating the volume of the cup, approximating the amount of water in
the pitcher, and predicting the behavior of water when we tilt the pitcher.
Very little attention in computer vision has been made to liquids and their
containers. In this paper, we study liquid containers and their contents, and
propose methods to estimate the volume of containers, approximate the amount of
liquid in them, and perform comparative volume estimations all from a single
RGB image. Furthermore, we show the results of the proposed model for
predicting the behavior of liquids inside containers when one tilts the
containers. We also introduce a new dataset of Containers Of liQuid contEnt
(COQE) that contains more than 5,000 images of 10,000 liquid containers in
context labelled with volume, amount of content, bounding box annotation, and
corresponding similar 3D CAD models.
Unsupervised Submodular Rank Aggregation on Score-based Permutations
Authors: Jun Qi, Xu Liu, Javier Tejedor, Shunsuke Kamijo
Date: 2017-07-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Information Retrieval, Retrieval, speech-recognition, Speech Recognition, Stochastic Optimization

Unsupervised rank aggregation on score-based permutations, which is widely
used in many applications, has not been deeply explored yet. This work studies
the use of submodular optimization for rank aggregation on score-based
permutations in an unsupervised way. Specifically, we propose an unsupervised
approach based on the Lovasz Bregman divergence for setting up linear
structured convex and nested structured concave objective functions. In
addition, stochastic optimization methods are applied in the training process
and efficient algorithms for inference can be guaranteed. The experimental
results from Information Retrieval, Combining Distributed Neural Networks,
Influencers in Social Networks, and Distributed Automatic Speech Recognition
tasks demonstrate the effectiveness of the proposed methods.
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images
Authors: Hao Zhou, Jin Sun, Yaser Yacoob, David W. Jacobs
Date: 2017-09-06 00:00:00
Proceeding: CVPR 2018
Tasks: Denoising, Image Forgery Detection, Intrinsic Image Decomposition, Lighting Estimation

Lighting estimation from face images is an important task and has
applications in many areas such as image editing, intrinsic image
decomposition, and image forgery detection. We propose to train a deep
Convolutional Neural Network (CNN) to regress lighting parameters from a single
face image. Lacking massive ground truth lighting labels for face images in the
wild, we use an existing method to estimate lighting parameters, which are
treated as ground truth with unknown noises. To alleviate the effect of such
noises, we utilize the idea of Generative Adversarial Networks (GAN) and
propose a Label Denoising Adversarial Network (LDAN) to make use of synthetic
data with accurate ground truth to help train a deep CNN for lighting
regression on real face images. Experiments show that our network outperforms
existing methods in producing consistent lighting parameters of different faces
under similar lighting conditions. Moreover, our method is 100,000 times faster
in execution time than prior optimization-based lighting estimation approaches.
Artificial Intelligence and Data Science in the Automotive Industry
Authors: Martin Hofmann, Florian Neukart, Thomas Bäck
Date: 2017-09-06 00:00:00
Tasks: BIG-bench Machine Learning, Marketing

Data science and machine learning are the key technologies when it comes to
the processes and products with automatic learning and optimization to be used
in the automotive industry of the future. This article defines the terms "data
science" (also referred to as "data analytics") and "machine learning" and how
they are related. In addition, it defines the term "optimizing analytics" and
illustrates the role of automatic optimization as a key technology in
combination with data analytics. It also uses examples to explain the way that
these technologies are currently being used in the automotive industry on the
basis of the major subprocesses in the automotive value chain (development,
procurement; logistics, production, marketing, sales and after-sales, connected
customer). Since the industry is just starting to explore the broad range of
potential uses for these technologies, visionary application examples are used
to illustrate the revolutionary possibilities that they offer. Finally, the
article demonstrates how these technologies can make the automotive industry
more efficient and enhance its customer focus throughout all its operations and
activities, extending from the product and its development process to the
customers and their connection to the product.
Energy saving for building heating via a simple and efficient model-free control design: First steps with computer simulations
Authors: Hassane Abouaïssa, Ola Alhaj Hasan, Cédric Join, Michel Fliess, Didier Defer
Date: 2017-08-12 00:00:00

The model-based control of building heating systems for energy saving
encounters severe physical, mathematical and calibration difficulties in the
numerous attempts that has been published until now. This topic is addressed
here via a new model-free control setting, where the need of any mathematical
description disappears. Several convincing computer simulations are presented.
Comparisons with classic PI controllers and flatness-based predictive control
are provided.
Source localization in an ocean waveguide using supervised machine learning
Authors: Haiqiang Niu, Emma Reeves, Peter Gerstoft
Date: 2017-01-29 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Source localization in ocean acoustics is posed as a machine learning problem
in which data-driven methods learn source ranges directly from observed
acoustic data. The pressure received by a vertical linear array is preprocessed
by constructing a normalized sample covariance matrix (SCM) and used as the
input. Three machine learning methods (feed-forward neural networks (FNN),
support vector machines (SVM) and random forests (RF)) are investigated in this
paper, with focus on the FNN. The range estimation problem is solved both as a
classification problem and as a regression problem by these three machine
learning algorithms. The results of range estimation for the Noise09 experiment
are compared for FNN, SVM, RF and conventional matched-field processing and
demonstrate the potential of machine learning for underwater source
localization..
The Do's and Don'ts for CNN-based Face Verification
Authors: Ankan Bansal, Carlos Castillo, Rajeev Ranjan, Rama Chellappa
Date: 2017-05-21 00:00:00
Tasks: Face Recognition, Face Verification

While the research community appears to have developed a consensus on the
methods of acquiring annotated data, design and training of CNNs, many
questions still remain to be answered. In this paper, we explore the following
questions that are critical to face recognition research: (i) Can we train on
still images and expect the systems to work on videos? (ii) Are deeper datasets
better than wider datasets? (iii) Does adding label noise lead to improvement
in performance of deep networks? (iv) Is alignment needed for face recognition?
We address these questions by training CNNs using CASIA-WebFace, UMDFaces, and
a new video dataset and testing on YouTube- Faces, IJB-A and a disjoint portion
of UMDFaces datasets. Our new data set, which will be made publicly available,
has 22,075 videos and 3,735,476 human annotated frames extracted from them.