pwc-archive/papers-with-abstracts huggingface dataset:
Function space analysis of deep learning representation layers
Authors: Oren Elisha, Shai Dekel
Date: 2017-10-09 00:00:00
Tasks: Clustering, Deep Learning, Representation Learning

In this paper we propose a function space approach to Representation Learning
and the analysis of the representation layers in deep learning architectures.
We show how to compute a weak-type Besov smoothness index that quantifies the
geometry of the clustering in the feature space. This approach was already
applied successfully to improve the performance of machine learning algorithms
such as the Random Forest and tree-based Gradient Boosting. Our experiments
demonstrate that in well-known and well-performing trained networks, the Besov
smoothness of the training set, measured in the corresponding hidden layer
feature map representation, increases from layer to layer. We also contribute
to the understanding of generalization by showing how the Besov smoothness of
the representations, decreases as we add more mis-labeling to the training
data. We hope this approach will contribute to the de-mystification of some
aspects of deep learning.
Multitask training with unlabeled data for end-to-end sign language fingerspelling recognition
Authors: Bowen Shi, Karen Livescu
Date: 2017-10-09 00:00:00
Tasks: Decoder

We address the problem of automatic American Sign Language fingerspelling
recognition from video. Prior work has largely relied on frame-level labels,
hand-crafted features, or other constraints, and has been hampered by the
scarcity of data for this task. We introduce a model for fingerspelling
recognition that addresses these issues. The model consists of an
auto-encoder-based feature extractor and an attention-based neural
encoder-decoder, which are trained jointly. The model receives a sequence of
image frames and outputs the fingerspelled word, without relying on any
frame-level training labels or hand-crafted features. In addition, the
auto-encoder subcomponent makes it possible to leverage unlabeled data to
improve the feature learning. The model achieves 11.6% and 4.4% absolute letter
accuracy improvement respectively in signer-independent and signer-adapted
fingerspelling recognition over previous approaches that required frame-level
training labels.
Exact Identification of a Quantum Change Point
Authors: Gael Sentís, John Calsamiglia, Ramon Munoz-Tapia
Date: 2017-07-24 00:00:00

The detection of change points is a pivotal task in statistical analysis. In
the quantum realm, it is a new primitive where one aims at identifying the
point where a source that supposedly prepares a sequence of particles in
identical quantum states starts preparing a mutated one. We obtain the optimal
procedure to identify the change point with certainty---naturally at the price
of having a certain probability of getting an inconclusive answer. We obtain
the analytical form of the optimal probability of successful identification for
any length of the particle sequence. We show that the conditional success
probabilities of identifying each possible change point show an unexpected
oscillatory behaviour. We also discuss local (online) protocols and compare
them with the optimal procedure.
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
Authors: Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet
Date: 2017-09-18 00:00:00
Tasks: regression

This paper presents a fast approach for penalized least squares (LS)
regression problems using a 2D Gaussian Markov random field (GMRF) prior. More
precisely, the computation of the proximity operator of the LS criterion
regularized by different GMRF potentials is formulated as solving a
Sylvester-like matrix equation. By exploiting the structural properties of
GMRFs, this matrix equation is solved columnwise in an analytical way. The
proposed algorithm can be embedded into a wide range of proximal algorithms to
solve LS regression problems including a convex penalty. Experiments carried
out in the case of a constrained LS regression problem arising in a
multichannel image processing application, provide evidence that an alternating
direction method of multipliers performs quite efficiently in this context.
Random Projection and Its Applications
Authors: Mahmoud Nabil
Date: 2017-10-09 00:00:00
Tasks: BIG-bench Machine Learning

Random Projection is a foundational research topic that connects a bunch of
machine learning algorithms under a similar mathematical basis. It is used to
reduce the dimensionality of the dataset by projecting the data points
efficiently to a smaller dimensions while preserving the original relative
distance between the data points. In this paper, we are intended to explain
random projection method, by explaining its mathematical background and
foundation, the applications that are currently adopting it, and an overview on
its current research perspective.
MSC: A Dataset for Macro-Management in StarCraft II
Authors: Huikai Wu, Yanqi Zong, Junge Zhang, Kaiqi Huang
Date: 2017-10-09 00:00:00
Tasks: Management, Real-Time Strategy Games, Starcraft, Starcraft II

Macro-management is an important problem in StarCraft, which has been studied for a long time. Various datasets together with assorted methods have been proposed in the last few years. But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, parsing and feature extraction procedures nor predefined training, validation and test set in some datasets. 2) Some datasets are only specified for certain tasks in macro-management. 3) Some datasets are either too small or don't have enough labeled data for modern machine learning algorithms such as deep neural networks. So most previous methods are trained with various features, evaluated on different test sets from the same or different datasets, making it difficult to be compared directly. To boost the research of macro-management in StarCraft, we release a new dataset MSC based on the platform SC2LE. MSC consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. We also split MSC into training, validation and test set for the convenience of evaluation and comparison. Besides the dataset, we propose a baseline model and present initial baseline results for global state evaluation and build order prediction, which are two of the key tasks in macro-management. Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II. Homepage: https://github.com/wuhuikai/MSC.
A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images
Authors: Zhun Fan, Jiewei Lu, Wenji Li, Caimin Wei, Han Huang, Xinye Cai, Xinjian Chen
Date: 2017-01-04 00:00:00
Tasks: Image Matting, Segmentation

In this paper, a hierarchical image matting model is proposed to extract
blood vessels from fundus images. More specifically, a hierarchical strategy
utilizing the continuity and extendibility of retinal blood vessels is
integrated into the image matting model for blood vessel segmentation. Normally
the matting models require the user specified trimap, which separates the input
image into three regions manually: the foreground, background and unknown
regions. However, since creating a user specified trimap is a tedious and
time-consuming task, region features of blood vessels are used to generate the
trimap automatically in this paper. The proposed model has low computational
complexity and outperforms many other state-ofart supervised and unsupervised
methods in terms of accuracy, which achieves a vessel segmentation accuracy of
96:0%, 95:7% and 95:1% in an average time of 10:72s, 15:74s and 50:71s on
images from three publicly available fundus image datasets DRIVE, STARE, and
CHASE DB1, respectively.
Handwritten digit string recognition by combination of residual network and RNN-CTC
Authors: Hongjian Zhan, Qingqing Wang, Yue Lu
Date: 2017-10-09 00:00:00

Recurrent neural network (RNN) and connectionist temporal classification
(CTC) have showed successes in many sequence labeling tasks with the strong
ability of dealing with the problems where the alignment between the inputs and
the target labels is unknown. Residual network is a new structure of
convolutional neural network and works well in various computer vision tasks.
In this paper, we take advantage of the architectures mentioned above to create
a new network for handwritten digit string recognition. First we design a
residual network to extract features from input images, then we employ a RNN to
model the contextual information within feature sequences and predict
recognition results. At the top of this network, a standard CTC is applied to
calculate the loss and yield the final results. These three parts compose an
end-to-end trainable network. The proposed new architecture achieves the
highest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates
89.75% and 91.14%, respectively. In addition, the experiments on a generated
captcha dataset which has much longer string length show the potential of the
proposed network to handle long strings.
Person Recognition in Personal Photo Collections
Authors: Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
Date: 2017-10-09 00:00:00
Tasks: Face Recognition, Person Recognition

People nowadays share large parts of their personal lives through social
media. Being able to automatically recognise people in personal photos may
greatly enhance user convenience by easing photo album organisation. For human
identification task, however, traditional focus of computer vision has been
face recognition and pedestrian re-identification. Person recognition in social
media photos sets new challenges for computer vision, including non-cooperative
subjects (e.g. backward viewpoints, unusual poses) and great changes in
appearance. To tackle this problem, we build a simple person recognition
framework that leverages convnet features from multiple image regions (head,
body, etc.). We propose new recognition scenarios that focus on the time and
appearance gap between training and testing samples. We present an in-depth
analysis of the importance of different features according to time and
viewpoint generalisability. In the process, we verify that our simple approach
achieves the state of the art result on the PIPA benchmark, arguably the
largest social media based benchmark for person recognition to date with
diverse poses, viewpoints, social groups, and events.
  Compared the conference version of the paper, this paper additionally
presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2
that combines the conference version method naeil and DeepID2+ to achieve state
of the art results even compared to post-conference works, (3) discussion of
related work since the conference version, (4) additional analysis including
the head viewpoint-wise breakdown of performance, and (5) results on the
open-world setup.
Deeper, Broader and Artier Domain Generalization
Authors: Da Li, Yongxin Yang, Yi-Zhe Song, Timothy M. Hospedales
Date: 2017-10-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Generalization

The problem of domain generalization is to learn from multiple training
domains, and extract a domain-agnostic model that can then be applied to an
unseen domain. Domain generalization (DG) has a clear motivation in contexts
where there are target domains with distinct characteristics, yet sparse data
for training. For example recognition in sketch images, which are distinctly
more abstract and rarer than photos. Nevertheless, DG methods have primarily
been evaluated on photo-only benchmarks focusing on alleviating the dataset
bias where both problems of domain distinctiveness and data sparsity can be
minimal. We argue that these benchmarks are overly straightforward, and show
that simple deep learning baselines perform surprisingly well on them. In this
paper, we make two main contributions: Firstly, we build upon the favorable
domain shift-robust properties of deep learning methods, and develop a low-rank
parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG
benchmark dataset covering photo, sketch, cartoon and painting domains. This is
both more practically relevant, and harder (bigger domain shift) than existing
benchmarks. The results show that our method outperforms existing DG
alternatives, and our dataset provides a more significant DG challenge to drive
future research.
full-FORCE: A Target-Based Method for Training Recurrent Networks
Authors: Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, L. F. Abbott
Date: 2017-10-09 00:00:00

Trained recurrent networks are powerful tools for modeling dynamic neural
computations. We present a target-based method for modifying the full
connectivity matrix of a recurrent network to train it to perform tasks
involving temporally complex input/output transformations. The method
introduces a second network during training to provide suitable "target"
dynamics useful for performing the task. Because it exploits the full recurrent
connectivity, the method produces networks that perform tasks with fewer
neurons and greater noise robustness than traditional least-squares (FORCE)
approaches. In addition, we show how introducing additional input signals into
the target-generating network, which act as task hints, greatly extends the
range of tasks that can be learned and provides control over the complexity and
nature of the dynamics of the trained, task-performing network.
Learning Graph Representations with Embedding Propagation
Authors: Alberto Garcia-Duran, Mathias Niepert
Date: 2017-10-09 00:00:00
Proceeding: NeurIPS 2017 12

We propose Embedding Propagation (EP), an unsupervised learning framework for
graph-structured data. EP learns vector representations of graphs by passing
two types of messages between neighboring nodes. Forward messages consist of
label representations such as representations of words and other attributes
associated with the nodes. Backward messages consist of gradients that result
from aggregating the label representations and applying a reconstruction loss.
Node representations are finally computed from the representation of their
labels. With significantly fewer parameters and hyperparameters an instance of
EP is competitive with and often outperforms state of the art unsupervised and
semi-supervised learning methods on a range of benchmark data sets.
Unifying Local and Global Change Detection in Dynamic Networks
Authors: Wenzhe Li, Dong Guo, Greg Ver Steeg, Aram Galstyan
Date: 2017-10-09 00:00:00
Tasks: Change Detection

Many real-world networks are complex dynamical systems, where both local
(e.g., changing node attributes) and global (e.g., changing network topology)
processes unfold over time. Local dynamics may provoke global changes in the
network, and the ability to detect such effects could have profound
implications for a number of real-world problems. Most existing techniques
focus individually on either local or global aspects of the problem or treat
the two in isolation from each other. In this paper we propose a novel network
model that simultaneously accounts for both local and global dynamics. To the
best of our knowledge, this is the first attempt at modeling and detecting
local and global change points on dynamic networks via a unified generative
framework. Our model is built upon the popular mixed membership stochastic
blockmodels (MMSB) with sparse co-evolving patterns. We derive an efficient
stochastic gradient Langevin dynamics (SGLD) sampler for our proposed model,
which allows it to scale to potentially very large networks. Finally, we
validate our model on both synthetic and real-world data and demonstrate its
superiority over several baselines.
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Authors: Heng Guo, Kaan Kara, Ce Zhang
Date: 2017-05-15 00:00:00

For Markov chain Monte Carlo methods, one of the greatest discrepancies
between theory and system is the scan order - while most theoretical
development on the mixing time analysis deals with random updates, real-world
systems are implemented with systematic scans. We bridge this gap for models
that exhibit a bipartite structure, including, most notably, the
Restricted/Deep Boltzmann Machine. The de facto implementation for these models
scans variables in a layerwise fashion. We show that the Gibbs sampler with a
layerwise alternating scan order has its relaxation time (in terms of epochs)
no larger than that of a random-update Gibbs sampler (in terms of variable
updates). We also construct examples to show that this bound is asymptotically
tight. Through standard inequalities, our result also implies a comparison on
the mixing times.
SGD for robot motion? The effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks
Authors: Martim Brandao, Kenji Hashimoto, Atsuo Takanishi
Date: 2017-10-09 00:00:00
Tasks: Stochastic Optimization

Trajectory optimization and posture generation are hard problems in robot
locomotion, which can be non-convex and have multiple local optima. Progress on
these problems is further hindered by a lack of open benchmarks, since
comparisons of different solutions are difficult to make. In this paper we
introduce a new benchmark for trajectory optimization and posture generation of
legged robots, using a pre-defined scenario, robot and constraints, as well as
evaluation criteria. We evaluate state-of-the-art trajectory optimization
algorithms based on sequential quadratic programming (SQP) on the benchmark, as
well as new stochastic and incremental optimization methods borrowed from the
large-scale machine learning literature. Interestingly we show that some of
these stochastic and incremental methods, which are based on stochastic
gradient descent (SGD), achieve higher success rates than SQP on tough
initializations. Inspired by this observation we also propose a new incremental
variant of SQP which updates only a random subset of the costs and constraints
at each iteration. The algorithm is the best performing in both success rate
and convergence speed, improving over SQP by up to 30% in both criteria. The
benchmark's resources and a solution evaluation script are made openly
available.
A Bottom Up Procedure for Text Line Segmentation of Latin Script
Authors: Himanshu Jain, Archana Praveen Kumar
Date: 2017-10-09 00:00:00
Tasks: Segmentation

In this paper we present a bottom up procedure for segmentation of text lines
written or printed in the Latin script. The proposed method uses a combination
of image morphology, feature extraction and Gaussian mixture model to perform
this task. The experimental results show the validity of the procedure.
An automatic deep learning approach for coronary artery calcium segmentation
Authors: G. Santini, D. Della Latta, N. Martini, G. Valvano, A. Gori, A. Ripoli, C. L. Susini, L. Landini, D. Chiappino
Date: 2017-10-09 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, General Classification, Specificity

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and
cardiovascular events. In this work we present a system for the automatic
quantification of calcium score in ECG-triggered non-contrast enhanced cardiac
computed tomography (CT) images. The proposed system uses a supervised deep
learning algorithm, i.e. convolutional neural network (CNN) for the
segmentation and classification of candidate lesions as coronary or not,
previously extracted in the region of the heart using a cardiac atlas. We
trained our network with 45 CT volumes; 18 volumes were used to validate the
model and 56 to test it. Individual lesions were detected with a sensitivity of
91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%;
comparing calcium score obtained by the system and calcium score manually
evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A
high agreement (Cohen's k = 0.879) between manual and automatic risk prediction
was also observed. These results demonstrated that convolutional neural
networks can be effectively applied for the automatic segmentation and
classification of coronary calcifications.
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
Authors: Alexander Richard, Hilde Kuehne, Juergen Gall
Date: 2017-03-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Segmentation, Temporal Action Segmentation, Weakly-supervised Learning

We present an approach for weakly supervised learning of human actions. Given
a set of videos and an ordered list of the occurring actions, the goal is to
infer start and end frames of the related action classes within the video and
to train the respective action classifiers without any need for hand labeled
frame boundaries. To address this task, we propose a combination of a
discriminative representation of subactions, modeled by a recurrent neural
network, and a coarse probabilistic model to allow for a temporal alignment and
inference over long sequences. While this system alone already generates good
results, we show that the performance can be further improved by approximating
the number of subactions to the characteristics of the different action
classes. To this end, we adapt the number of subaction classes by iterating
realignment and reestimation during training. The proposed system is evaluated
on two benchmark datasets, the Breakfast and the Hollywood extended dataset,
showing a competitive performance on various weak learning tasks such as
temporal action segmentation and action alignment.
Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment
Authors: Umberto Fugiglando, Emanuele Massaro, Paolo Santi, Sebastiano Milardo, Kacem Abida, Rainer Stahlmann, Florian Netter, Carlo Ratti
Date: 2017-10-09 00:00:00
Tasks: Clustering, General Classification

Cars can nowadays record several thousands of signals through the CAN bus
technology and potentially provide real-time information on the car, the driver
and the surrounding environment. This paper proposes a new method for the
analysis and classification of driver behavior using a selected subset of CAN
bus signals, specifically gas pedal position, brake pedal pressure, steering
wheel angle, steering wheel momentum, velocity, RPM, frontal and lateral
acceleration. Data has been collected in a completely uncontrolled experiment,
where 64 people drove 10 cars for or a total of over 2000 driving trips without
any type of pre-determined driving instruction on a wide variety of road
scenarios. We propose an unsupervised learning technique that clusters drivers
in different groups, and offers a validation method to test the robustness of
clustering in a wide range of experimental settings. The minimal amount of data
needed to preserve robust driver clustering is also computed. The presented
study provides a new methodology for near-real-time classification of driver
behavior in uncontrolled environments.