pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
Authors: Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
Date: 2018-07-14 00:00:00
Tasks: compressed sensing, Computational Efficiency, Matrix Completion, Model Discovery, regression, Variable Selection

Regularized regression problems are ubiquitous in statistical modeling,
signal processing, and machine learning. Sparse regression in particular has
been instrumental in scientific model discovery, including compressed sensing
applications, variable selection, and high-dimensional analysis. We propose a
broad framework for sparse relaxed regularized regression, called SR3. The key
idea is to solve a relaxation of the regularized problem, which has three
advantages over the state-of-the-art: (1) solutions of the relaxed problem are
superior with respect to errors, false positives, and conditioning, (2)
relaxation allows extremely fast algorithms for both convex and nonconvex
formulations, and (3) the methods apply to composite regularizers such as total
variation (TV) and its nonconvex variants. We demonstrate the advantages of SR3
(computational efficiency, higher accuracy, faster convergence rates, greater
flexibility) across a range of regularized regression problems with synthetic
and real data, including applications in compressed sensing, LASSO, matrix
completion, TV regularization, and group sparsity. To promote reproducible
research, we also provide a companion MATLAB package that implements these
examples.
3D human pose estimation from depth maps using a deep combination of poses
Authors: Manuel J. Marin-Jimenez, Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, Rafael Medina-Carnicer
Date: 2018-07-14 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Many real-world applications require the estimation of human body joints for
higher-level tasks as, for example, human behaviour understanding. In recent
years, depth sensors have become a popular approach to obtain three-dimensional
information. The depth maps generated by these sensors provide information that
can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps
employing a Deep Learning approach. We propose a model, named Deep Depth Pose
(DDP), which receives a depth map containing a person and a set of predefined
3D prototype poses and returns the 3D position of the body joints of the
person. In particular, DDP is defined as a ConvNet that computes the specific
weights needed to linearly combine the prototypes for the given input. We have
thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which
respectively depict realistic and synthetic samples, defining a new
state-of-the-art on them.
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
Authors: Masoud Abdi, Ehsan Abbasnejad, Chee Peng Lim, Saeid Nahavandi
Date: 2018-07-14 00:00:00
Proceeding: British Machine Vision Conference 2018 2018 7
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

Tremendous amounts of expensive annotated data are a vital ingredient for
state-of-the-art 3d hand pose estimation. Therefore, synthetic data has been
popularized as annotations are automatically available. However, models trained
only with synthetic samples do not generalize to real data, mainly due to the
gap between the distribution of synthetic and real data. In this paper, we
propose a novel method that seeks to predict the 3d position of the hand using
both synthetic and partially-labeled real data. Accordingly, we form a shared
latent space between three modalities: synthetic depth image, real depth image,
and pose. We demonstrate that by carefully learning the shared latent space, we
can find a regression model that is able to generalize to real data. As such,
we show that our method produces accurate predictions in both semi-supervised
and unsupervised settings. Additionally, the proposed model is capable of
generating novel, meaningful, and consistent samples from all of the three
domains. We evaluate our method qualitatively and quantitively on two highly
competitive benchmarks (i.e., NYU and ICVL) and demonstrate its superiority
over the state-of-the-art methods. The source code will be made available at
https://github.com/masabdi/LSPS.
Non-local RoIs for Instance Segmentation
Authors: Shou-Yao Roy Tseng, Hwann-Tzong Chen, Shao-Heng Tai, Tyng-Luh Liu
Date: 2018-07-14 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We introduce the concept of Non-Local RoI (NL-RoI) Block as a generic and
flexible module that can be seamlessly adapted into different Mask R-CNN heads
for various tasks. Mask R-CNN treats RoIs (Regions of Interest) independently
and performs the prediction based on individual object bounding boxes. However,
the correlation between objects may provide useful information for detection
and segmentation. The proposed NL-RoI Block enables each RoI to refer to all
other RoIs' information, and results in a simple, low-cost but effective
module. Our experimental results show that generalizations with NL-RoI Blocks
can improve the performance of Mask R-CNN for instance segmentation on the
Robust Vision Challenge benchmarks.
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
Authors: Raj Dabre, Atsushi Fujita
Date: 2018-07-14 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

In neural machine translation (NMT), the most common practice is to stack a
number of recurrent or feed-forward layers in the encoder and the decoder. As a
result, the addition of each new layer improves the translation quality
significantly. However, this also leads to a significant increase in the number
of parameters. In this paper, we propose to share parameters across all the
layers thereby leading to a recurrently stacked NMT model. We empirically show
that the translation quality of a model that recurrently stacks a single layer
6 times is comparable to the translation quality of a model that stacks 6
separate layers. We also show that using pseudo-parallel corpora by
back-translation leads to further significant improvements in translation
quality.
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
Authors: Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar
Date: 2018-07-14 00:00:00
Tasks: BIG-bench Machine Learning

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group,
is a top-level ontology that provides a set of classes, properties, and
restrictions for representing and interchanging information on machine learning
algorithms, datasets, and experiments. It can be easily extended and
specialized and it is also mapped to other more domain-specific ontologies
developed in the area of machine learning and data mining. In this paper we
overview existing state-of-the-art machine learning interchange formats and
present the first release of ML-Schema, a canonical format resulted of more
than seven years of experience among different research institutions. We argue
that exposing semantics of machine learning algorithms, models, and experiments
through a canonical format may pave the way to better interpretability and to
realistically achieve the full interoperability of experiments regardless of
platform or adopted workflow solution.
Adversarially Learned Mixture Model
Authors: Andrew Jesson, Cécile Low-Kam, Tanya Nair, Florian Soudan, Florent Chandelier, Nicolas Chapados
Date: 2018-07-14 00:00:00
Tasks: Clustering, model

The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering. The AMM is the first adversarially optimized method to model the conditional dependence between inferred continuous and categorical latent variables. Experiments on the MNIST and SVHN datasets show that the AMM allows for semantic separation of complex data when little or no labeled data is available. The AMM achieves a state-of-the-art unsupervised clustering error rate of 2.86% on the MNIST dataset. A semi-supervised extension of the AMM yields competitive results on the SVHN dataset.
Generalization in quasi-periodic environments
Authors: Giovanni Bellettini, Alessandro Betti, Marco Gori
Date: 2018-07-14 00:00:00

By and large the behavior of stochastic gradient is regarded as a challenging
problem, and it is often presented in the framework of statistical machine
learning. This paper offers a novel view on the analysis of on-line models of
learning that arises when dealing with a generalized version of stochastic
gradient that is based on dissipative dynamics. In order to face the complex
evolution of these models, a systematic treatment is proposed which is based on
energy balance equations that are derived by means of the Caldirola-Kanai (CK)
Hamiltonian. According to these equations, learning can be regarded as an
ordering process which corresponds with the decrement of the loss function.
Finally, the main results established in this paper is that in the case of
quasi-periodic environments, where the pattern novelty is progressively limited
as time goes by, the system dynamics yields an asymptotically consistent
solution in the weight space, that is the solution maps similar patterns to the
same decision.
Real-Time Shape Tracking of Facial Landmarks
Authors: Hyungjoon Kim, Hyeonwoo Kim, Eenjun Hwang
Date: 2018-07-14 00:00:00
Tasks: Semantic Segmentation

Detection of facial landmarks and accurate tracking of their shape are
essential in real-time virtual makeup applications, where users can see the
makeups effect by moving their face in different directions. Typical face
tracking techniques detect diverse facial landmarks and track them using a
point tracker such as the Kanade-Lucas-Tomasi (KLT) point tracker. Typically, 5
or 64 points are used for tracking a face. Even though these points are
sufficient to track the approximate locations of facial landmarks, they are not
sufficient to track the exact shape of facial landmarks. In this paper, we
propose a method that can track the exact shape of facial landmarks in
real-time by combining a deep learning technique and a point tracker. We detect
facial landmarks accurately using SegNet, which performs semantic segmentation
based on deep learning. Edge points of detected landmarks are tracked using the
KLT point tracker. In spite of its popularity, the KLT point tracker suffers
from the point loss problem. We solve this problem by executing SegNet
periodically to calculate the shape of facial landmarks. That is, by combining
the two techniques, we can avoid the computational overhead of SegNet for
real-time shape tracking and the point loss problem of the KLT point tracker.
We performed several experiments to evaluate the performance of our method and
report some of the results herein.
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches
Authors: Jie Liu, Yu Rong, Martin Takac, Junzhou Huang
Date: 2018-07-14 00:00:00

This paper proposes a framework of L-BFGS based on the (approximate)
second-order information with stochastic batches, as a novel approach to the
finite-sum minimization problems. Different from the classical L-BFGS where
stochastic batches lead to instability, we use a smooth estimate for the
evaluations of the gradient differences while achieving acceleration by
well-scaling the initial Hessians. We provide theoretical analyses for both
convex and nonconvex cases. In addition, we demonstrate that within the popular
applications of least-square and cross-entropy losses, the algorithm admits a
simple implementation in the distributed environment. Numerical experiments
support the efficiency of our algorithms.
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
Authors: Daniel H. Noronha, Bahar Salehpour, Steven J. E. Wilton
Date: 2018-07-14 00:00:00
Tasks: BIG-bench Machine Learning, High-Level Synthesis, Translation

Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an
important role in the acceleration of Machine Learning applications. Initial
specification of machine learning applications are often done using a
high-level Python-oriented framework such as Tensorflow, followed by a manual
translation to either C or RTL for synthesis using vendor tools. This manual
translation step is time-consuming and requires expertise that limit the
applicability of FPGAs in this important domain. In this paper, we present an
open-source tool-flow that maps numerical computation models written in
Tensorflow to synthesizable hardware. Unlike other tools, which are often
constrained by a small number of inflexible templates, our flow uses Google's
XLA compiler which emits LLVM code directly from a Tensorflow specification.
This LLVM code can then be used with a high-level synthesis tool to
automatically generate hardware. We show that our flow allows users to generate
Deep Neural Networks with very few lines of Python code.
How Do Classifiers Induce Agents To Invest Effort Strategically?
Authors: Jon Kleinberg, Manish Raghavan
Date: 2018-07-13 00:00:00
Tasks: Decision Making

Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcomes they receive, and we give a tight characterization of when such agents can be incentivized to invest specified forms of effort into improving their outcomes as opposed to "gaming" the classifier. We show that whenever any "reasonable" mechanism can do so, a simple linear mechanism suffices.
Generative Adversarial Privacy
Authors: Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
Date: 2018-07-13 00:00:00
Proceeding: ICLR 2019 5

We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. We also evaluate GAP's performance on the GENKI face database.
Neural Networks Regularization Through Representation Learning
Authors: Soufiane Belharbi
Date: 2018-07-13 00:00:00
Tasks: Data Augmentation, Representation Learning, Transfer Learning

Neural network models and deep models are one of the leading and state of the
art models in machine learning. Most successful deep neural models are the ones
with many layers which highly increases their number of parameters. Training
such models requires a large number of training samples which is not always
available. One of the fundamental issues in neural networks is overfitting
which is the issue tackled in this thesis. Such problem often occurs when the
training of large models is performed using few training samples. Many
approaches have been proposed to prevent the network from overfitting and
improve its generalization performance such as data augmentation, early
stopping, parameters sharing, unsupervised learning, dropout, batch
normalization, etc.
  In this thesis, we tackle the neural network overfitting issue from a
representation learning perspective by considering the situation where few
training samples are available which is the case of many real world
applications. We propose three contributions. The first one presented in
chapter 2 is dedicated to dealing with structured output problems to perform
multivariate regression when the output variable y contains structural
dependencies between its components. The second contribution described in
chapter 3 deals with the classification task where we propose to exploit prior
knowledge about the internal representation of the hidden layers in neural
networks. Our last contribution presented in chapter 4 showed the interest of
transfer learning in applications where only few samples are available. In this
contribution, we provide an automatic system based on such learning scheme with
an application to medical domain. In this application, the task consists in
localizing the third lumbar vertebra in a 3D CT scan. This work has been done
in collaboration with the clinic Rouen Henri Becquerel Center who provided us
with data.
Survey on Deep Learning Techniques for Person Re-Identification Task
Authors: Bahram Lavi, Mehdi Fatan Serj, Ihsan Ullah
Date: 2018-07-13 00:00:00
Tasks: Deep Learning, Person Re-Identification, Retrieval, Survey

Intelligent video-surveillance is currently an active research field in
computer vision and machine learning techniques. It provides useful tools for
surveillance operators and forensic video investigators. Person
re-identification (PReID) is one among these tools. It consists of recognizing
whether an individual has already been observed over a camera in a network or
not. This tool can also be employed in various possible applications such as
off-line retrieval of all the video-sequences showing an individual of interest
whose image is given a query, and online pedestrian tracking over multiple
camera views. To this aim, many techniques have been proposed to increase the
performance of PReID. Among the systems, many researchers utilized deep neural
networks (DNNs) because of their better performance and fast execution at test
time. Our objective is to provide for future researchers the work being done on
PReID to date. Therefore, we summarized state-of-the-art DNN models being used
for this task. A brief description of each model along with their evaluation on
a set of benchmark datasets is given. Finally, a detailed comparison is
provided among these models followed by some limitations that can work as
guidelines for future research.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
Authors: Adam Czajka, Daniel Moreira, Kevin W. Bowyer, Patrick J. Flynn
Date: 2018-07-13 00:00:00
Tasks: Domain Adaptation, Iris Recognition, Texture Classification

Binarized statistical image features (BSIF) have been successfully used for
texture analysis in many computer vision tasks, including iris recognition and
biometric presentation attack detection. One important point is that all
applications of BSIF in iris recognition have used the original BSIF filters,
which were trained on image patches extracted from natural images. This paper
tests the question of whether domain-specific BSIF can give better performance
than the default BSIF. The second important point is in the selection of image
patches to use in training for BSIF. Can image patches derived from
eye-tracking experiments, in which humans perform an iris recognition task,
give better performance than random patches? Our results say that (1)
domain-specific BSIF features can out-perform the default BSIF features, and
(2) selecting image patches in a task-specific manner guided by human
performance can out-perform selecting random patches. These results are
important because BSIF is often regarded as a generic texture tool that does
not need any domain adaptation, and human-task-guided selection of patches for
training has never (to our knowledge) been done. This paper follows the
reproducible research requirements, and the new iris-domain-specific BSIF
filters, the patches used in filter training, the database used in testing and
the source codes of the designed iris recognition method are made available
along with this paper to facilitate applications of this concept.
Channel Charting: Locating Users within the Radio Environment using Channel State Information
Authors: Christoph Studer, Saïd Medjkouh, Emre Gönültaş, Tom Goldstein, Olav Tirkkonen
Date: 2018-07-13 00:00:00
Tasks: Dimensionality Reduction, Scheduling

We propose channel charting (CC), a novel framework in which a multi-antenna
network element learns a chart of the radio geometry in its surrounding area.
The channel chart captures the local spatial geometry of the area so that
points that are close in space will also be close in the channel chart and vice
versa. CC works in a fully unsupervised manner, i.e., learning is only based on
channel state information (CSI) that is passively collected at a single point
in space, but from multiple transmit locations in the area over time. The
method then extracts channel features that characterize large-scale fading
properties of the wireless channel. Finally, the channel charts are generated
with tools from dimensionality reduction, manifold learning, and deep neural
networks. The network element performing CC may be, for example, a
multi-antenna base-station in a cellular system and the charted area in the
served cell. Logical relationships related to the position and movement of a
transmitter, e.g., a user equipment (UE), in the cell can then be directly
deduced from comparing measured radio channel characteristics to the channel
chart. The unsupervised nature of CC enables a range of new applications in UE
localization, network planning, user scheduling, multipoint connectivity,
hand-over, cell search, user grouping, and other cognitive tasks that rely on
CSI and UE movement relative to the base-station, without the need of
information from global navigation satellite systems.
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Authors: Daniel Moreira, Mateusz Trokielewicz, Adam Czajka, Kevin W. Bowyer, Patrick J. Flynn
Date: 2018-07-13 00:00:00
Tasks: General Classification, Iris Recognition, Pupil Dilation

This paper advances the state of the art in human examination of iris images
by (1) assessing the impact of different iris conditions in identity
verification, and (2) introducing an annotation step that improves the accuracy
of people's decisions. In a first experimental session, 114 subjects were asked
to decide if pairs of iris images depict the same eye (genuine pairs) or two
distinct eyes (impostor pairs). The image pairs sampled six conditions: (1)
easy for algorithms to classify, (2) difficult for algorithms to classify, (3)
large difference in pupil dilation, (4) disease-affected eyes, (5) identical
twins, and (6) post-mortem samples. In a second session, 85 of the 114 subjects
were asked to annotate matching and non-matching regions that supported their
decisions. Subjects were allowed to change their initial classification as a
result of the annotation process. Results suggest that: (a) people improve
their identity verification accuracy when asked to annotate matching and
non-matching regions between the pair of images, (b) images depicting the same
eye with large difference in pupil dilation were the most challenging to
subjects, but benefited well from the annotation-driven classification, (c)
humans performed better than iris recognition algorithms when verifying genuine
pairs of post-mortem and disease-affected eyes (i.e., samples showing
deformations that go beyond the distortions of a healthy iris due to pupil
dilation), and (d) annotation does not improve accuracy of analyzing images
from identical twins, which remain confusing for people.