pwc-archive/papers-with-abstracts huggingface dataset:
Robust Multi-view Pedestrian Tracking Using Neural Networks
Authors: Md Zahangir Alom, Tarek M. Taha
Date: 2017-04-21 00:00:00
Tasks: General Classification, Pedestrian Detection

In this paper, we present a real-time robust multi-view pedestrian detection
and tracking system for video surveillance using neural networks which can be
used in dynamic environments. The proposed system consists of two phases:
multi-view pedestrian detection and tracking. First, pedestrian detection
utilizes background subtraction to segment the foreground blob. An adaptive
background subtraction method where each of the pixel of input image models as
a mixture of Gaussians and uses an on-line approximation to update the model
applies to extract the foreground region. The Gaussian distributions are then
evaluated to determine which are most likely to result from a background
process. This method produces a steady, real-time tracker in outdoor
environment that consistently deals with changes of lighting condition, and
long-term scene change. Second, the Tracking is performed at two phases:
pedestrian classification and tracking the individual subject. A sliding window
is applied on foreground binary image to select an input window which is used
for selecting the input image patches from actually input frame. The neural
networks is used for classification with PHOG features. Finally, a Kalman
filter is applied to calculate the subsequent step for tracking that aims at
finding the exact position of pedestrians in an input image. The experimental
result shows that the proposed approach yields promising performance on
multi-view pedestrian detection and tracking on different benchmark datasets.
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Authors: Sam Gross, Marc'Aurelio Ranzato, Arthur Szlam
Date: 2017-04-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: GPU, Mixture-of-Experts

Training convolutional networks (CNN's) that fit on a single GPU with
minibatch stochastic gradient descent has become effective in practice.
However, there is still no effective method for training large CNN's that do
not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
this work we show that a simple hard mixture of experts model can be
efficiently trained to good effect on large scale hashtag (multilabel)
prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991,
Collobert et. al. 2003), but in the past, researchers have had to devise
sophisticated methods to deal with data fragmentation. We show empirically that
modern weakly supervised data sets are large enough to support naive
partitioning schemes where each data point is assigned to a single expert.
Because the experts are independent, training them in parallel is easy, and
evaluation is cheap for the size of the model. Furthermore, we show that we can
use a single decoding layer for all the experts, allowing a unified feature
embedding space. We demonstrate that it is feasible (and in fact relatively
painless) to train far larger models than could be practically trained with
standard CNN architectures, and that the extra capacity can be well used on
current datasets.
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Authors: Jason Fries, Sen Wu, Alex Ratner, Christopher Ré
Date: 2017-04-20 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Weakly-Supervised Named Entity Recognition

We present SwellShark, a framework for building biomedical named entity
recognition (NER) systems quickly and without hand-labeled data. Our approach
views biomedical resources like lexicons as function primitives for
autogenerating weak supervision. We then use a generative model to unify and
denoise this supervision and construct large-scale, probabilistically labeled
datasets for training high-accuracy NER taggers. In three biomedical NER tasks,
SwellShark achieves competitive scores with state-of-the-art supervised
benchmarks using no hand-labeled training data. In a drug name extraction task
using patient medical records, one domain expert using SwellShark achieved
within 5.1% of a crowdsourced annotation approach -- which originally utilized
20 teams over the course of several weeks -- in 24 hours.
An Optimal Dimensionality Multi-shell Sampling Scheme with Accurate and Efficient Transforms for Diffusion MRI
Authors: Alice P. Bates, Zubair Khalid, Jason D. McEwen, Rodney A. Kennedy
Date: 2017-04-20 00:00:00
Tasks: Diffusion  MRI

This paper proposes a multi-shell sampling scheme and corresponding
transforms for the accurate reconstruction of the diffusion signal in diffusion
MRI by expansion in the spherical polar Fourier (SPF) basis. The sampling
scheme uses an optimal number of samples, equal to the degrees of freedom of
the band-limited diffusion signal in the SPF domain, and allows for
computationally efficient reconstruction. We use synthetic data sets to
demonstrate that the proposed scheme allows for greater reconstruction accuracy
of the diffusion signal than the multi-shell sampling schemes obtained using
the generalised electrostatic energy minimisation (gEEM) method used in the
Human Connectome Project. We also demonstrate that the proposed sampling scheme
allows for increased angular discrimination and improved rotational invariance
of reconstruction accuracy than the gEEM schemes.
Distributed Statistical Estimation and Rates of Convergence in Normal Approximation
Authors: Stanislav Minsker, Nate Strawn
Date: 2017-04-09 00:00:00

This paper presents a class of new algorithms for distributed statistical
estimation that exploit divide-and-conquer approach. We show that one of the
key benefits of the divide-and-conquer strategy is robustness, an important
characteristic for large distributed systems. We establish connections between
performance of these distributed algorithms and the rates of convergence in
normal approximation, and prove non-asymptotic deviations guarantees, as well
as limit theorems, for the resulting estimators. Our techniques are illustrated
through several examples: in particular, we obtain new results for the
median-of-means estimator, as well as provide performance guarantees for
distributed maximum likelihood estimation.
Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ
Authors: Jason S. Kessler
Date: 2017-03-02 00:00:00

Scattertext is an open source tool for visualizing linguistic variation
between document categories in a language-independent way. The tool presents a
scatterplot, where each axis corresponds to the rank-frequency a term occurs in
a category of documents. Through a tie-breaking strategy, the tool is able to
display thousands of visible term-representing points and find space to legibly
label hundreds of them. Scattertext also lends itself to a query-based
visualization of how the use of terms with similar embeddings differs between
document categories, as well as a visualization for comparing the importance
scores of bag-of-words features to univariate metrics.
Low-complexity Image and Video Coding Based on an Approximate Discrete Tchebichef Transform
Authors: P. A. M. Oliveira, R. J. Cintra, F. M. Bayer, S. Kulasekera, A. Madanayake, V. A. Coutinho
Date: 2016-09-24 00:00:00
Tasks: Video Compression

The usage of linear transformations has great relevance for data decorrelation applications, like image and video compression. In that sense, the discrete Tchebichef transform (DTT) possesses useful coding and decorrelation properties. The DTT transform kernel does not depend on the input data and fast algorithms can be developed to real time applications. However, the DTT fast algorithm presented in literature possess high computational complexity. In this work, we introduce a new low-complexity approximation for the DTT. The fast algorithm of the proposed transform is multiplication-free and requires a reduced number of additions and bit-shifting operations. Image and video compression simulations in popular standards shows good performance of the proposed transform. Regarding hardware resource consumption for FPGA shows 43.1% reduction of configurable logic blocks and ASIC place and route realization shows 57.7% reduction in the area-time figure when compared with the 2-D version of the exact DTT.
Identifying First-person Camera Wearers in Third-person Videos
Authors: Chenyou Fan, Jang-Won Lee, Mingze Xu, Krishna Kumar Singh, Yong Jae Lee, David J. Crandall, Michael S. Ryoo
Date: 2017-04-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Activity Recognition, Object Tracking, Scene Understanding, Triplet

We consider scenarios in which we wish to perform joint scene understanding,
object tracking, activity recognition, and other tasks in environments in which
multiple people are wearing body-worn cameras while a third-person static
camera also captures the scene. To do this, we need to establish person-level
correspondences across first- and third-person videos, which is challenging
because the camera wearer is not visible from his/her own egocentric video,
preventing the use of direct feature matching. In this paper, we propose a new
semi-Siamese Convolutional Neural Network architecture to address this novel
challenge. We formulate the problem as learning a joint embedding space for
first- and third-person videos that considers both spatial- and motion-domain
cues. A new triplet loss function is designed to minimize the distance between
correct first- and third-person matches while maximizing the distance between
incorrect ones. This end-to-end approach performs significantly better than
several baselines, in part by learning the first- and third-person features
optimized for matching jointly with the distance measure itself.
Estimating Quality in Multi-Objective Bandits Optimization
Authors: Audrey Durand, Christian Gagné
Date: 2017-01-04 00:00:00
Tasks: Thompson Sampling

Many real-world applications are characterized by a number of conflicting
performance measures. As optimizing in a multi-objective setting leads to a set
of non-dominated solutions, a preference function is required for selecting the
solution with the appropriate trade-off between the objectives. The question
is: how good do estimations of these objectives have to be in order for the
solution maximizing the preference function to remain unchanged? In this paper,
we introduce the concept of preference radius to characterize the robustness of
the preference function and provide guidelines for controlling the quality of
estimations in the multi-objective setting. More specifically, we provide a
general formulation of multi-objective optimization under the bandits setting.
We show how the preference radius relates to the optimal gap and we use this
concept to provide a theoretical analysis of the Thompson sampling algorithm
from multivariate normal priors. We finally present experiments to support the
theoretical results and highlight the fact that one cannot simply scalarize
multi-objective problems into single-objective problems.
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Authors: Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, Manmohan Chandraker
Date: 2016-12-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Instance Segmentation, Semantic Segmentation

Monocular 3D object parsing is highly desirable in various scenarios
including occlusion reasoning and holistic scene interpretation. We present a
deep convolutional neural network (CNN) architecture to localize semantic parts
in 2D image and 3D space while inferring their visibility states, given a
single RGB image. Our key insight is to exploit domain knowledge to regularize
the network by deeply supervising its hidden layers, in order to sequentially
infer intermediate concepts associated with the final task. To acquire training
data in desired quantities with ground truth 3D shape and relevant concepts, we
render 3D object CAD models to generate large-scale synthetic data and simulate
challenging occlusion configurations between objects. We train the network only
on synthetic data and demonstrate state-of-the-art performances on real image
benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and
IKEA for 2D and 3D keypoint localization and instance segmentation. The
empirical results substantiate the utility of our deep supervision scheme by
demonstrating effective transfer of knowledge from synthetic data to real
images, resulting in less overfitting compared to standard end-to-end training.
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
Authors: Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt
Date: 2017-04-20 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The management of invasive mechanical ventilation, and the regulation of
sedation and analgesia during ventilation, constitutes a major part of the care
of patients admitted to intensive care units. Both prolonged dependence on
mechanical ventilation and premature extubation are associated with increased
risk of complications and higher hospital costs, but clinical opinion on the
best protocol for weaning patients off of a ventilator varies. This work aims
to develop a decision support tool that uses available patient information to
predict time-to-extubation readiness and to recommend a personalized regime of
sedation dosage and ventilator support. To this end, we use off-policy
reinforcement learning algorithms to determine the best action at a given
patient state from sub-optimal historical ICU data. We compare treatment
policies from fitted Q-iteration with extremely randomized trees and with
feedforward neural networks, and demonstrate that the policies learnt show
promise in recommending weaning protocols with improved outcomes, in terms of
minimizing rates of reintubation and regulating physiological stability.
Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech Detection
Authors: Pengfei Sun, Jun Qin
Date: 2016-11-01 00:00:00

In this letter, we propose enhanced factored three way restricted Boltzmann
machines (EFTW-RBMs) for speech detection. The proposed model incorporates
conditional feature learning by multiplying the dynamical state of the third
unit, which allows a modulation over the visible-hidden node pairs. Instead of
stacking previous frames of speech as the third unit in a recursive manner, the
correlation related weighting coefficients are assigned to the contextual
neighboring frames. Specifically, a threshold function is designed to capture
the long-term features and blend the globally stored speech structure. A
factored low rank approximation is introduced to reduce the parameters of the
three-dimensional interaction tensor, on which non-negative constraint is
imposed to address the sparsity characteristic. The validations through the
area-under-ROC-curve (AUC) and signal distortion ratio (SDR) show that our
approach outperforms several existing 1D and 2D (i.e., time and time-frequency
domain) speech detection algorithms in various noisy environments.
Trust Region Policy Optimization
Authors: John Schulman, Sergey Levine, Philipp Moritz, Michael. I. Jordan, Pieter Abbeel
Date: 2015-02-19 00:00:00
Tasks: Atari Games, Policy Gradient Methods

We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters.
Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency
Authors: Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, Jitendra Malik
Date: 2017-04-20 00:00:00
Proceeding: CVPR 2017 7

We study the notion of consistency between a 3D shape and a 2D observation
and propose a differentiable formulation which allows computing gradients of
the 3D shape given an observation from an arbitrary view. We do so by
reformulating view consistency using a differentiable ray consistency (DRC)
term. We show that this formulation can be incorporated in a learning framework
to leverage different types of multi-view observations e.g. foreground masks,
depth, color images, semantics etc. as supervision for learning single-view 3D
prediction. We present empirical analysis of our technique in a controlled
setting. We also show that this approach allows us to improve over existing
techniques for single-view reconstruction of objects from the PASCAL VOC
dataset.
Embedding Watermarks into Deep Neural Networks
Authors: Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, Shin'ichi Satoh
Date: 2017-01-15 00:00:00

Deep neural networks have recently achieved significant progress. Sharing
trained models of these deep neural networks is very important in the rapid
progress of researching or developing deep neural network systems. At the same
time, it is necessary to protect the rights of shared trained models. To this
end, we propose to use a digital watermarking technology to protect
intellectual property or detect intellectual property infringement of trained
models. Firstly, we formulate a new problem: embedding watermarks into deep
neural networks. We also define requirements, embedding situations, and attack
types for watermarking to deep neural networks. Secondly, we propose a general
framework to embed a watermark into model parameters using a parameter
regularizer. Our approach does not hurt the performance of networks into which
a watermark is embedded. Finally, we perform comprehensive experiments to
reveal the potential of watermarking to deep neural networks as a basis of this
new problem. We show that our framework can embed a watermark in the situations
of training a network from scratch, fine-tuning, and distilling without hurting
the performance of a deep neural network. The embedded watermark does not
disappear even after fine-tuning or parameter pruning; the watermark completely
remains even after removing 65% of parameters were pruned. The implementation
of this research is: https://github.com/yu4u/dnn-watermark
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access
Authors: Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
Date: 2016-09-03 00:00:00
Proceeding: ACL 2017 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, World Knowledge

This paper proposes KB-InfoBot -- a multi-turn dialogue agent which helps
users search Knowledge Bases (KBs) without composing complicated queries. Such
goal-oriented dialogue agents typically need to interact with an external
database to access real-world knowledge. Previous systems achieved this by
issuing a symbolic query to the KB to retrieve entries based on their
attributes. However, such symbolic operations break the differentiability of
the system and prevent end-to-end training of neural dialogue agents. In this
paper, we address this limitation by replacing symbolic queries with an induced
"soft" posterior distribution over the KB that indicates which entities the
user is interested in. Integrating the soft retrieval process with a
reinforcement learner leads to higher task success rate and reward in both
simulations and against real users. We also present a fully neural end-to-end
agent, trained entirely from user feedback, and discuss its application towards
personalized dialogue agents. The source code is available at
https://github.com/MiuLab/KB-InfoBot.
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
Authors: Ji He, Mari Ostendorf, Xiaodong He
Date: 2017-04-20 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper addresses the problem of predicting popularity of comments in an
online discussion forum using reinforcement learning, particularly addressing
two challenges that arise from having natural language state and action spaces.
First, the state representation, which characterizes the history of comments
tracked in a discussion at a particular point, is augmented to incorporate the
global context represented by discussions on world events available in an
external knowledge source. Second, a two-stage Q-learning framework is
introduced, making it feasible to search the combinatorial action space while
also accounting for redundancy among sub-actions. We experiment with five
Reddit communities, showing that the two methods improve over previous reported
results on this task.
ADMM Penalty Parameter Selection by Residual Balancing
Authors: Brendt Wohlberg
Date: 2017-04-20 00:00:00

Appropriate selection of the penalty parameter is crucial to obtaining good
performance from the Alternating Direction Method of Multipliers (ADMM). While
analytic results for optimal selection of this parameter are very limited,
there is a heuristic method that appears to be relatively successful in a
number of different problems. The contribution of this paper is to demonstrate
that their is a potentially serious flaw in this heuristic approach, and to
propose a modification that at least partially addresses it.
Dynamic Graph Convolutional Networks
Authors: Franco Manessi, Alessandro Rozza, Mario Manzo
Date: 2017-04-20 00:00:00
Tasks: General Classification

Many different classification tasks need to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
the vertices/edges of each graph may change during time. Our goal is to jointly
exploit structured data and temporal information through the use of a neural
network model. To the best of our knowledge, this task has not been addressed
using these kind of architectures. For this reason, we propose two novel
approaches, which combine Long Short-Term Memory networks and Graph
Convolutional Networks to learn long short-term dependencies together with
graph structure. The quality of our methods is confirmed by the promising
results achieved.
Intrusion Prevention and Detection in Grid Computing - The ALICE Case
Authors: Andres Gomez, Camilo Lara, Udo Kebschull
Date: 2017-04-20 00:00:00

Grids allow users flexible on-demand usage of computing resources through
remote communication networks. A remarkable example of a Grid in High Energy
Physics (HEP) research is used in the ALICE experiment at European Organization
for Nuclear Research CERN. Physicists can submit jobs used to process the huge
amount of particle collision data produced by the Large Hadron Collider (LHC).
Grids face complex security challenges. They are interesting targets for
attackers seeking for huge computational resources. Since users can execute
arbitrary code in the worker nodes on the Grid sites, special care should be
put in this environment. Automatic tools to harden and monitor this scenario
are required. Currently, there is no integrated solution for such requirement.
This paper describes a new security framework to allow execution of job
payloads in a sandboxed context. It also allows process behavior monitoring to
detect intrusions, even when new attack methods or zero day vulnerabilities are
exploited, by a Machine Learning approach. We plan to implement the proposed
framework as a software prototype that will be tested as a component of the
ALICE Grid middleware.