pwc-archive/papers-with-abstracts huggingface dataset:
A Compressed Sensing Approach for Distribution Matching
Authors: Mohamad Dia, Vahid Aref, Laurent Schmalen
Date: 2018-04-02 00:00:00
Tasks: Bayesian Inference, compressed sensing, Quantization

In this work, we formulate the fixed-length distribution matching as a
Bayesian inference problem. Our proposed solution is inspired from the
compressed sensing paradigm and the sparse superposition (SS) codes. First, we
introduce sparsity in the binary source via position modulation (PM). We then
present a simple and exact matcher based on Gaussian signal quantization. At
the receiver, the dematcher exploits the sparsity in the source and performs
low-complexity dematching based on generalized approximate message-passing
(GAMP). We show that GAMP dematcher and spatial coupling lead to asymptotically
optimal performance, in the sense that the rate tends to the entropy of the
target distribution with vanishing reconstruction error in a proper limit.
Furthermore, we assess the performance of the dematcher on practical
Hadamard-based operators. A remarkable feature of our proposed solution is the
possibility to: i) perform matching at the symbol level (nonbinary); ii)
perform joint channel coding and matching.
Graph Learning-Convolutional Networks
Authors: Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang
Date: 2018-11-25 00:00:00
Tasks: graph construction, Graph Learning

Recently, graph Convolutional Neural Networks (graph CNNs) have been widely
used for graph data representation and semi-supervised learning tasks. However,
existing graph CNNs generally use a fixed graph which may be not optimal for
semi-supervised learning tasks. In this paper, we propose a novel Graph
Learning-Convolutional Network (GLCN) for graph data representation and
semi-supervised learning. The aim of GLCN is to learn an optimal graph
structure that best serves graph CNNs for semi-supervised learning by
integrating both graph learning and graph convolution together in a unified
network architecture. The main advantage is that in GLCN, both given labels and
the estimated labels are incorporated and thus can provide useful 'weakly'
supervised information to refine (or learn) the graph construction and also to
facilitate the graph convolution operation in GLCN for unknown label
estimation. Experimental results on seven benchmarks demonstrate that GLCN
significantly outperforms state-of-the-art traditional fixed structure based
graph CNNs.
Learning Sound Events From Webly Labeled Data
Authors: Anurag Kumar, Ankit Shah, Alex Hauptmann, Bhiksha Raj
Date: 2018-11-25 00:00:00
Proceeding: 28th International Joint Conference on Artificial Intelligence 2019 8
Tasks: Event Detection, Sound Event Detection, Transfer Learning

In the last couple of years, weakly labeled learning for sound events has
turned out to be an exciting approach for audio event detection. In this work,
we introduce webly labeled learning for sound events in which we aim to remove
human supervision altogether from the learning process. We first develop a
method of obtaining labeled audio data from the web (albeit noisy), in which no
manual labeling is involved. We then describe deep learning methods to
efficiently learn from these webly labeled audio recordings. In our proposed
system, WeblyNet, two deep neural networks co-teach each other to robustly
learn from webly labeled data, leading to around 17% relative improvement over
the baseline method. The method also involves transfer learning to obtain
efficient representations.
On-chip learning for domain wall synapse based Fully Connected Neural Network
Authors: Apoorv Dankar, Anand Verma, Utkarsh Saxena, Divya Kaushik, Shouri Chatterjee, Debanjan Bhowmik
Date: 2018-11-25 00:00:00

Spintronic devices are considered as promising candidates in implementing
neuromorphic systems or hardware neural networks, which are expected to perform
better than other existing computing systems for certain data classification
and regression tasks. In this paper, we have designed a feedforward Fully
Connected Neural Network (FCNN) with no hidden layer using spin orbit torque
driven domain wall devices as synapses and transistor based analog circuits as
neurons. A feedback circuit is also designed using transistors, which at every
iteration computes the change in weights of the synapses needed to train the
network using Stochastic Gradient Descent (SGD) method. Subsequently it sends
write current pulses to the domain wall based synaptic devices which move the
domain walls and updates the weights of the synapses. Through a combination of
micromagnetic simulations, analog circuit simulations and numerically solving
FCNN training equations, we demonstrate "on-chip" training of the designed FCNN
on the MNIST database of handwritten digits in this paper. We report the
training and test accuracies, energy consumed in the synaptic devices for the
training and possible issues with hardware implementation of FCNN that can
limit its test accuracy.
Practical optimal registration of terrestrial LiDAR scan pairs
Authors: Zhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos, Konrad Schindler
Date: 2018-11-25 00:00:00
Tasks: Point Cloud Registration

Point cloud registration is a fundamental problem in 3D scanning. In this
paper, we address the frequent special case of registering terrestrial LiDAR
scans (or, more generally, levelled point clouds). Many current solutions still
rely on the Iterative Closest Point (ICP) method or other heuristic procedures,
which require good initializations to succeed and/or provide no guarantees of
success. On the other hand, exact or optimal registration algorithms can
compute the best possible solution without requiring initializations; however,
they are currently too slow to be practical in realistic applications.
  Existing optimal approaches ignore the fact that in routine use the relative
rotations between scans are constrained to the azimuth, via the built-in level
compensation in LiDAR scanners. We propose a novel, optimal and computationally
efficient registration method for this 4DOF scenario. Our approach operates on
candidate 3D keypoint correspondences, and contains two main steps: (1) a
deterministic selection scheme that significantly reduces the candidate
correspondence set in a way that is guaranteed to preserve the optimal
solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel
polynomial-time subroutine for 1D rotation search, that quickly finds the
optimal alignment for the reduced set. We demonstrate the practicality of our
method on realistic point clouds from multiple LiDAR surveys.
Deep RNN Framework for Visual Sequential Applications
Authors: Bo Pang, Kaiwen Zha, Hanwen Cao, Chen Shi, Cewu Lu
Date: 2018-11-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Future prediction, SSIM, Video Classification

Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural framework that can be stacked deep effectively. There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions; the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources. We provide empirical evidence to show that our deep RNN framework is easy to optimize and can gain accuracy from the increased depth on several visual sequence problems. On these tasks, we evaluate our deep RNN framework with 15 layers, 7* than conventional RNN networks, but it is still easy to train. Our deep framework achieves more than 11% relative improvements over shallow RNN models on Kinetics, UCF-101, and HMDB-51 for video classification. For auxiliary annotation, after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM, the performance improves by 14.7%. For video future prediction, our deep RNN improves the state-of-the-art shallow model's performance by 2.4% on PSNR and SSIM. The code and trained models are published accompanied by this paper: https://github.com/BoPang1996/Deep-RNN-Framework.
Intersectionality: Multiple Group Fairness in Expectation Constraints
Authors: Jack Fitzsimons, Michael Osborne, Stephen Roberts
Date: 2018-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, regression

Group fairness is an important concern for machine learning researchers,
developers, and regulators. However, the strictness to which models must be
constrained to be considered fair is still under debate. The focus of this work
is on constraining the expected outcome of subpopulations in kernel regression
and, in particular, decision tree regression, with application to random
forests, boosted trees and other ensemble models. While individual constraints
were previously addressed, this work addresses concerns about incorporating
multiple constraints simultaneously. The proposed solution does not affect the
order of computational or memory complexity of the decision trees and is easily
integrated into models post training.
Glottal Closure Instants Detection From Pathological Acoustic Speech Signal Using Deep Learning
Authors: Gurunath Reddy M, Tanumay Mandal, Krothapalli Sreenivasa Rao
Date: 2018-11-25 00:00:00
Tasks: General Classification

In this paper, we propose a classification based glottal closure instants
(GCI) detection from pathological acoustic speech signal, which finds many
applications in vocal disorder analysis. Till date, GCI for pathological
disorder is extracted from laryngeal (glottal source) signal recorded from
Electroglottograph, a dedicated device designed to measure the vocal folds
vibration around the larynx. We have created a pathological dataset which
consists of simultaneous recordings of glottal source and acoustic speech
signal of six different disorders from vocal disordered patients. The GCI
locations are manually annotated for disorder analysis and supervised learning.
We have proposed convolutional neural network based GCI detection method by
fusing deep acoustic speech and linear prediction residual features for robust
GCI detection. The experimental results showed that the proposed method is
significantly better than the state-of-the-art GCI detection methods.
Online Newton Step Algorithm with Estimated Gradient
Authors: Binbin Liu, Jundong Li, Yunquan Song, Xijun Liang, Ling Jian, Huan Liu
Date: 2018-11-25 00:00:00

Online learning with limited information feedback (bandit) tries to solve the
problem where an online learner receives partial feedback information from the
environment in the course of learning. Under this setting, Flaxman et al.[8]
extended Zinkevich's classical Online Gradient Descent (OGD) algorithm [29] by
proposing the Online Gradient Descent with Expected Gradient (OGDEG) algorithm.
Specifically, it uses a simple trick to approximate the gradient of the loss
function $f_t$ by evaluating it at a single point and bounds the expected
regret as $\mathcal{O}(T^{5/6})$ [8], where the number of rounds is $T$.
Meanwhile, past research efforts have shown that compared with the first-order
algorithms, second-order online learning algorithms such as Online Newton Step
(ONS) [11] can significantly accelerate the convergence rate of traditional
online learning algorithms. Motivated by this, this paper aims to exploit the
second-order information to speed up the convergence of the OGDEG algorithm. In
particular, we extend the ONS algorithm with the trick of expected gradient and
develop a novel second-order online learning algorithm, i.e., Online Newton
Step with Expected Gradient (ONSEG). Theoretically, we show that the proposed
ONSEG algorithm significantly reduces the expected regret of OGDEG algorithm
from $\mathcal{O}(T^{5/6})$ to $\mathcal{O}(T^{2/3})$ in the bandit feedback
scenario. Empirically, we further demonstrate the advantages of the proposed
algorithm on multiple real-world datasets.
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images
Authors: Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein, Li Fei-Fei
Date: 2018-11-25 00:00:00
Tasks: Action Recognition, Activity Recognition, Privacy Preserving, Super-Resolution, Temporal Action Localization

Computer-vision hospital systems can greatly assist healthcare workers and
improve medical facility treatment, but often face patient resistance due to
the perceived intrusiveness and violation of privacy associated with visual
surveillance. We downsample video frames to extremely low resolutions to
degrade private information from surveillance videos. We measure the amount of
activity-recognition information retained in low resolution depth images, and
also apply a privately-trained DCSCN super-resolution model to enhance the
utility of our images. We implement our techniques with two actual
healthcare-surveillance scenarios, hand-hygiene compliance and ICU
activity-logging, and show that our privacy-preserving techniques preserve
enough information for realistic healthcare tasks.
Loop Closure Detection with RGB-D Feature Pyramid Siamese Networks
Authors: Zhang Qianhao, Alexander Mai, Joseph Menke, Allen Yang
Date: 2018-11-25 00:00:00
Tasks: Loop Closure Detection, Simultaneous Localization and Mapping

In visual Simultaneous Localization And Mapping (SLAM), detecting loop
closures has been an important but difficult task. Currently, most solutions
are based on the bag-of-words approach. Yet the possibility of deep neural
network application to this task has not been fully explored due to the lack of
appropriate architecture design and of sufficient training data. In this paper
we demonstrate the applicability of deep neural networks by addressing both
issues. Specifically we show that a feature pyramid Siamese neural network can
achieve state-of-the-art performance on pairwise loop closure detection. The
network is trained and tested on large-scale RGB-D datasets with a novel
automatic loop closure labeling algorithm. Each image pair is labelled by how
much the images overlap, allowing loop closure to be computed directly rather
than by labor intensive manual labeling. We present an algorithm to adopt any
large-scale generic RGB-D dataset for use in training deep loop-closure
networks. We show for the first time that deep neural networks are capable of
detecting loop closures, and we provide a method for generating large-scale
datasets for use in evaluating and training loop closure detectors.
Guided Feature Selection for Deep Visual Odometry
Authors: Fei Xue, Qiuyuan Wang, Xin Wang, Wei Dong, Junqiu Wang, Hongbin Zha
Date: 2018-11-25 00:00:00
Tasks: feature selection, Monocular Visual Odometry, Translation, Visual Odometry

We present a novel end-to-end visual odometry architecture with guided
feature selection based on deep convolutional recurrent neural networks.
Different from current monocular visual odometry methods, our approach is
established on the intuition that features contribute discriminately to
different motion patterns. Specifically, we propose a dual-branch recurrent
network to learn the rotation and translation separately by leveraging current
Convolutional Neural Network (CNN) for feature representation and Recurrent
Neural Network (RNN) for image sequence reasoning. To enhance the ability of
feature selection, we further introduce an effective context-aware guidance
mechanism to force each branch to distill related information for specific
motion pattern explicitly. Experiments demonstrate that on the prevalent KITTI
and ICL_NUIM benchmarks, our method outperforms current state-of-the-art model-
and learning-based methods for both decoupled and joint camera pose recovery.
PCGAN: Partition-Controlled Human Image Generation
Authors: Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou
Date: 2018-11-25 00:00:00
Tasks: Image Generation

Human image generation is a very challenging task since it is affected by
many factors. Many human image generation methods focus on generating human
images conditioned on a given pose, while the generated backgrounds are often
blurred.In this paper,we propose a novel Partition-Controlled GAN to generate
human images according to target pose and background. Firstly, human poses in
the given images are extracted, and foreground/background are partitioned for
further use. Secondly, we extract and fuse appearance features, pose features
and background features to generate the desired images. Experiments on
Market-1501 and DeepFashion datasets show that our model not only generates
realistic human images but also produce the human pose and background as we
want. Extensive experiments on COCO and LIP datasets indicate the potential of
our method.
Average-Case Information Complexity of Learning
Authors: Ido Nachum, Amir Yehudayoff
Date: 2018-11-25 00:00:00

How many bits of information are revealed by a learning algorithm for a
concept class of VC-dimension $d$? Previous works have shown that even for
$d=1$ the amount of information may be unbounded (tend to $\infty$ with the
universe size). Can it be that all concepts in the class require leaking a
large amount of information? We show that typically concepts do not require
leakage. There exists a proper learning algorithm that reveals $O(d)$ bits of
information for most concepts in the class. This result is a special case of a
more general phenomenon we explore. If there is a low information learner when
the algorithm {\em knows} the underlying distribution on inputs, then there is
a learner that reveals little information on an average concept {\em without
knowing} the distribution on inputs.
An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview
Authors: Bo Zhang, Bin Chen, Jinyu Yang, Wenjing Yang, Jiankang Zhang
Date: 2018-11-25 00:00:00

Motivated by Shannon's model and recent rehabilitation of self-supervised
artificial intelligence having a "World Model", this paper propose an unified
intelligence-communication (UIC) model for describing a single agent and any
multi-agent system.
  Firstly, the environment is modelled as the generic communication channel
between agents. Secondly, the UIC model adopts a learning-agent model for
unifying several well-adopted agent architecture, e.g. rule-based agent model
in complex adaptive systems, layered model for describing human-level
intelligence, world-model based agent model. The model may also provide an
unified approach to investigate a multi-agent system (MAS) having multiple
action-perception modalities, e.g. explicitly information transfer and implicit
information transfer.
  This treatise would be divided into three parts, and this first part provides
an overview of the UIC model without introducing cumbersome mathematical
analysis and optimizations. In the second part of this treatise, case studies
with quantitative analysis driven by the UIC model would be provided,
exemplifying the adoption of the UIC model in multi-agent system. Specifically,
two representative cases would be studied, namely the analysis of a natural
multi-agent system, as well as the co-design of communication, perception and
action in an artificial multi-agent system. In the third part of this treatise,
the paper provides further insights and future research directions motivated by
the UIC model, such as unification of single intelligence and collective
intelligence, a possible explanation of intelligence emergence and a dual model
for agent-environment intelligence hypothesis.
  Notes: This paper is a Previewed Version, the extended full-version would be
released after being accepted.
A Method for Analysis of Patient Speech in Dialogue for Dementia Detection
Authors: Saturnino Luz, Sofia de la Fuente, Pierre Albert
Date: 2018-11-25 00:00:00

We present an approach to automatic detection of Alzheimer's type dementia
based on characteristics of spontaneous spoken language dialogue consisting of
interviews recorded in natural settings. The proposed method employs additive
logistic regression (a machine learning boosting method) on content-free
features extracted from dialogical interaction to build a predictive model. The
model training data consisted of 21 dialogues between patients with Alzheimer's
and interviewers, and 17 dialogues between patients with other health
conditions and interviewers. Features analysed included speech rate,
turn-taking patterns and other speech parameters. Despite relying solely on
content-free features, our method obtains overall accuracy of 86.5\%, a result
comparable to those of state-of-the-art methods that employ more complex
lexical, syntactic and semantic features. While further investigation is
needed, the fact that we were able to obtain promising results using only
features that can be easily extracted from spontaneous dialogues suggests the
possibility of designing non-invasive and low-cost mental health monitoring
tools for use at scale.
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation
Authors: Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Hui Tang, Yufan Xue, Xiaohui Xie, Yen-Yu Lin, Wei Fan
Date: 2018-11-25 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

Hand pose estimation from a monocular RGB image is an important but challenging task. The main factor affecting its performance is the lack of a sufficiently large training dataset with accurate hand-keypoint annotations. In this work, we circumvent this problem by proposing an effective method for generating realistic hand poses and show that state-of-the-art algorithms for hand pose estimation can be greatly improved by utilizing the generated hand poses as training data. Specifically, we first adopt an augmented reality (AR) simulator to synthesize hand poses with accurate hand-keypoint labels. Although the synthetic hand poses come with precise joint labels, eliminating the need of manual annotations, they look unnatural and are not the ideal training data. To produce more realistic hand poses, we propose to blend a synthetic hand pose with a real background, such as arms and sleeves. To this end, we develop tonality-alignment generative adversarial networks (TAGANs), which align the tonality and color distributions between synthetic hand poses and real backgrounds, and can generate high quality hand poses. We evaluate TAGAN on three benchmarks, including the RHP, STB, and CMU-PS hand pose datasets. With the aid of the synthesized poses, our method performs favorably against the state-of-the-arts in both 2D and 3D hand pose estimations.