pwc-archive/papers-with-abstracts huggingface dataset:
Pull Message Passing for Nonparametric Belief Propagation
Authors: Karthik Desingh, Anthony Opipari, Odest Chadwicke Jenkins
Date: 2018-07-27 00:00:00

We present a "pull" approach to approximate products of Gaussian mixtures
within message updates for Nonparametric Belief Propagation (NBP) inference.
Existing NBP methods often represent messages between continuous-valued latent
variables as Gaussian mixture models. To avoid computational intractability in
loopy graphs, NBP necessitates an approximation of the product of such
mixtures. Sampling-based product approximations have shown effectiveness for
NBP inference. However, such approximations used within the traditional "push"
message update procedures quickly become computationally prohibitive for
multi-modal distributions over high-dimensional variables. In contrast, we
propose a "pull" method, as the Pull Message Passing for Nonparametric Belief
propagation (PMPNBP) algorithm, and demonstrate its viability for efficient
inference. We report results using an experiment from an existing NBP method,
PAMPAS, for inferring the pose of an articulated structure in clutter. Results
from this illustrative problem found PMPNBP has a greater ability to
efficiently scale the number of components in its mixtures and, consequently,
improve inference accuracy.
Adversarial Open-World Person Re-Identification
Authors: Xiang Li, An-Cong Wu, Wei-Shi Zheng
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Person Re-Identification

In a typical real-world application of re-id, a watch-list (gallery set) of a
handful of target people (e.g. suspects) to track around a large volume of
non-target people are demanded across camera views, and this is called the
open-world person re-id. Different from conventional (closed-world) person
re-id, a large portion of probe samples are not from target people in the
open-world setting. And, it always happens that a non-target person would look
similar to a target one and therefore would seriously challenge a re-id system.
In this work, we introduce a deep open-world group-based person re-id model
based on adversarial learning to alleviate the attack problem caused by similar
non-target people. The main idea is learning to attack feature extractor on the
target people by using GAN to generate very target-like images (imposters), and
in the meantime the model will make the feature extractor learn to tolerate the
attack by discriminative learning so as to realize group-based verification.
The framework we proposed is called the adversarial open-world person
re-identification, and this is realized by our Adversarial PersonNet (APN) that
jointly learns a generator, a person discriminator, a target discriminator and
a feature extractor, where the feature extractor and target discriminator share
the same weights so as to makes the feature extractor learn to tolerate the
attack by imposters for better group-based verification. While open-world
person re-id is challenging, we show for the first time that the
adversarial-based approach helps stabilize person re-id system under imposter
attack more effectively.
Interpreting recurrent neural networks behaviour via excitable network attractors
Authors: Andrea Ceni, Peter Ashwin, Lorenzo Livi
Date: 2018-07-27 00:00:00
Tasks: BIG-bench Machine Learning

Introduction: Machine learning provides fundamental tools both for scientific
research and for the development of technologies with significant impact on
society. It provides methods that facilitate the discovery of regularities in
data and that give predictions without explicit knowledge of the rules
governing a system. However, a price is paid for exploiting such flexibility:
machine learning methods are typically black-boxes where it is difficult to
fully understand what the machine is doing or how it is operating. This poses
constraints on the applicability and explainability of such methods. Methods:
Our research aims to open the black-box of recurrent neural networks, an
important family of neural networks used for processing sequential data. We
propose a novel methodology that provides a mechanistic interpretation of
behaviour when solving a computational task. Our methodology uses mathematical
constructs called excitable network attractors, which are invariant sets in
phase space composed of stable attractors and excitable connections between
them. Results and Discussion: As the behaviour of recurrent neural networks
depends both on training and on inputs to the system, we introduce an algorithm
to extract network attractors directly from the trajectory of a neural network
while solving tasks. Simulations conducted on a controlled benchmark task
confirm the relevance of these attractors for interpreting the behaviour of
recurrent neural networks, at least for tasks that involve learning a finite
number of stable states and transitions between them.
A Deep Learning Framework for Automatic Diagnosis in Lung Cancer
Authors: Nikolay Burlutskiy, Feng Gu, Lena Kajland Wilen, Max Backman, Patrick Micke
Date: 2018-07-27 00:00:00
Tasks: Deep Learning

We developed a deep learning framework that helps to automatically identify
and segment lung cancer areas in patients' tissue specimens. The study was
based on a cohort of lung cancer patients operated at the Uppsala University
Hospital. The tissues were reviewed by lung pathologists and then the cores
were compiled to tissue micro-arrays (TMAs). For experiments, hematoxylin-eosin
stained slides from 712 patients were scanned and then manually annotated. Then
these scans and annotations were used to train segmentation models of the
developed framework. The performance of the developed deep learning framework
was evaluated on fully annotated TMA cores from 178 patients reaching
pixel-wise precision of 0.80 and recall of 0.86. Finally, publicly available
Stanford TMA cores were used to demonstrate high performance of the framework
qualitatively.
AXNet: ApproXimate computing using an end-to-end trainable neural network
Authors: Zhenghao Peng, Xuyang Chen, Chengwen Xu, Naifeng Jing, Xiaoyao Liang, Cewu Lu, Li Jiang
Date: 2018-07-27 00:00:00
Tasks: Multi-Task Learning, Philosophy

Neural network based approximate computing is a universal architecture
promising to gain tremendous energy-efficiency for many error resilient
applications. To guarantee the approximation quality, existing works deploy two
neural networks (NNs), e.g., an approximator and a predictor. The approximator
provides the approximate results, while the predictor predicts whether the
input data is safe to approximate with the given quality requirement. However,
it is non-trivial and time-consuming to make these two neural network
coordinate---they have different optimization objectives---by training them
separately. This paper proposes a novel neural network structure---AXNet---to
fuse two NNs to a holistic end-to-end trainable NN. Leveraging the philosophy
of multi-task learning, AXNet can tremendously improve the invocation
(proportion of safe-to-approximate samples) and reduce the approximation error.
The training effort also decrease significantly. Experiment results show 50.7%
more invocation and substantial cuts of training time when compared to existing
neural network based approximate computing framework.
Acceleration through Optimistic No-Regret Dynamics
Authors: Jun-Kun Wang, Jacob Abernethy
Date: 2018-07-27 00:00:00
Proceeding: NeurIPS 2018 12

We consider the problem of minimizing a smooth convex function by reducing
the optimization to computing the Nash equilibrium of a particular zero-sum
convex-concave game. Zero-sum games can be solved using online learning
dynamics, where a classical technique involves simulating two no-regret
algorithms that play against each other and, after $T$ rounds, the average
iterate is guaranteed to solve the original optimization problem with error
decaying as $O(\log T/T)$. In this paper we show that the technique can be
enhanced to a rate of $O(1/T^2)$ by extending recent work \cite{RS13,SALS15}
that leverages \textit{optimistic learning} to speed up equilibrium
computation. The resulting optimization algorithm derived from this analysis
coincides \textit{exactly} with the well-known \NA \cite{N83a} method, and
indeed the same story allows us to recover several variants of the Nesterov's
algorithm via small tweaks. We are also able to establish the accelerated
linear rate for a function which is both strongly-convex and smooth. This
methodology unifies a number of different iterative optimization methods: we
show that the \HB algorithm is precisely the non-optimistic variant of \NA, and
recent prior work already established a similar perspective on \FW
\cite{AW17,ALLW18}.
Rob-GAN: Generator, Discriminator, and Adversarial Attacker
Authors: Xuanqing Liu, Cho-Jui Hsieh
Date: 2018-07-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Attack, Generative Adversarial Network, Image Generation

We study two important concepts in adversarial deep learning---adversarial
training and generative adversarial network (GAN). Adversarial training is the
technique used to improve the robustness of discriminator by combining
adversarial attacker and discriminator in the training phase. GAN is commonly
used for image generation by jointly optimizing discriminator and generator. We
show these two concepts are indeed closely related and can be used to
strengthen each other---adding a generator to the adversarial training
procedure can improve the robustness of discriminators, and adding an
adversarial attack to GAN training can improve the convergence speed and lead
to better generators. Combining these two insights, we develop a framework
called Rob-GAN to jointly optimize generator and discriminator in the presence
of adversarial attacks---the generator generates fake images to fool
discriminator; the adversarial attacker perturbs real images to fool the
discriminator, and the discriminator wants to minimize loss under fake and
adversarial images. Through this end-to-end training procedure, we are able to
simultaneously improve the convergence speed of GAN training, the quality of
synthetic images, and the robustness of discriminator under strong adversarial
attacks. Experimental results demonstrate that the obtained classifier is more
robust than the state-of-the-art adversarial training approach, and the
generator outperforms SN-GAN on ImageNet-143.
Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities
Authors: Qiyu Kang, Wee Peng Tay
Date: 2018-07-27 00:00:00

Workers participating in a crowdsourcing platform can have a wide range of
abilities and interests. An important problem in crowdsourcing is the task
recommendation problem, in which tasks that best match a particular worker's
preferences and reliabilities are recommended to that worker. A task
recommendation scheme that assigns tasks more likely to be accepted by a worker
who is more likely to complete it reliably results in better performance for
the task requester. Without prior information about a worker, his preferences
and reliabilities need to be learned over time. In this paper, we propose a
multi-armed bandit (MAB) framework to learn a worker's preferences and his
reliabilities for different categories of tasks. However, unlike the classical
MAB problem, the reward from the worker's completion of a task is unobservable.
We therefore include the use of gold tasks (i.e., tasks whose solutions are
known \emph{a priori} and which do not produce any rewards) in our task
recommendation procedure. Our model could be viewed as a new variant of MAB, in
which the random rewards can only be observed at those time steps where gold
tasks are used, and the accuracy of estimating the expected reward of
recommending a task to a worker depends on the number of gold tasks used. We
show that the optimal regret is $O(\sqrt{n})$, where $n$ is the number of tasks
recommended to the worker. We develop three task recommendation strategies to
determine the number of gold tasks for different task categories, and show that
they are order optimal. Simulations verify the efficiency of our approaches.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
Authors: Spandan Madan, Zoya Bylinskii, Matthew Tancik, Adrià Recasens, Kimberli Zhong, Sami Alsheikh, Hanspeter Pfister, Aude Oliva, Fredo Durand
Date: 2018-07-27 00:00:00
Tasks: Synthetic Data Generation

Widely used in news, business, and educational media, infographics are
handcrafted to effectively communicate messages about complex and often
abstract topics including `ways to conserve the environment' and `understanding
the financial crisis'. Composed of stylistically and semantically diverse
visual and textual elements, infographics pose new challenges for computer
vision. While automatic text extraction works well on infographics, computer
vision approaches trained on natural images fail to identify the stand-alone
visual elements in infographics, or `icons'. To bridge this representation gap,
we propose a synthetic data generation strategy: we augment background patches
in infographics from our Visually29K dataset with Internet-scraped icons which
we use as training data for an icon proposal mechanism. On a test set of 1K
annotated infographics, icons are located with 38% precision and 34% recall
(the best model trained with natural images achieves 14% precision and 7%
recall). Combining our icon proposals with icon classification and text
extraction, we present a multi-modal summarization application. Our application
takes an infographic as input and automatically produces text tags and visual
hashtags that are textually and visually representative of the infographic's
topics respectively.
Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
Authors: Eunji Chong, Nataniel Ruiz, Yongxin Wang, Yun Zhang, Agata Rozga, James Rehg
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Multi-Task Learning

This paper addresses the challenging problem of estimating the general visual
attention of people in images. Our proposed method is designed to work across
multiple naturalistic social scenarios and provides a full picture of the
subject's attention and gaze. In contrast, earlier works on gaze and attention
estimation have focused on constrained problems in more specific contexts. In
particular, our model explicitly represents the gaze direction and handles
out-of-frame gaze targets. We leverage three different datasets using a
multi-task learning approach. We evaluate our method on widely used benchmarks
for single-tasks such as gaze angle estimation and attention-within-an-image,
as well as on the new challenging task of generalized visual attention
prediction. In addition, we have created extended annotations for the MMDB and
GazeFollow datasets which are used in our experiments, which we will publicly
release.
Understanding V2V Driving Scenarios through Traffic Primitives
Authors: Wenshuo Wang, Weiyang Zhang, Ding Zhao
Date: 2018-07-27 00:00:00
Tasks: Clustering, Decision Making, Dynamic Time Warping

Semantically understanding complex drivers' encountering behavior, wherein
two or multiple vehicles are spatially close to each other, does potentially
benefit autonomous car's decision-making design. This paper presents a
framework of analyzing various encountering behaviors through decomposing
driving encounter data into small building blocks, called driving primitives,
using nonparametric Bayesian learning (NPBL) approaches, which offers a
flexible way to gain an insight into the complex driving encounters without any
prerequisite knowledge. The effectiveness of our proposed primitive-based
framework is validated based on 976 naturalistic driving encounters, from which
more than 4000 driving primitives are learned using NPBL - a sticky HDP-HMM,
combined a hidden Markov model (HMM) with a hierarchical Dirichlet process
(HDP). After that, a dynamic time warping method integrated with k-means
clustering is then developed to cluster all these extracted driving primitives
into groups. Experimental results find that there exist 20 kinds of driving
primitives capable of representing the basic components of driving encounters
in our database. This primitive-based analysis methodology potentially reveals
underlying information of vehicle-vehicle encounters for self-driving
applications.
Fusion Network for Face-based Age Estimation
Authors: Haoyi Wang, Xingjie Wei, Victor Sanchez, Chang-Tsun Li
Date: 2018-07-27 00:00:00
Tasks: Age Estimation, MORPH

Convolutional Neural Networks (CNN) have been applied to age-related research
as the core framework. Although faces are composed of numerous facial
attributes, most works with CNNs still consider a face as a typical object and
do not pay enough attention to facial regions that carry age-specific feature
for this particular task. In this paper, we propose a novel CNN architecture
called Fusion Network (FusionNet) to tackle the age estimation problem. Apart
from the whole face image, the FusionNet successively takes several
age-specific facial patches as part of the input to emphasize the age-specific
features. Through experiments, we show that the FusionNet significantly
outperforms other state-of-the-art models on the MORPH II benchmark.
W-TALC: Weakly-supervised Temporal Activity Localization and Classification
Authors: Sujoy Paul, Sourya Roy, Amit K. Roy-Chowdhury
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, General Classification, Weakly Supervised Action Localization

Most activity localization methods in the literature suffer from the burden
of frame-wise annotation requirement. Learning from weak labels may be a
potential solution towards reducing such manual labeling effort. Recent years
have witnessed a substantial influx of tagged videos on the Internet, which can
serve as a rich source of weakly-supervised training data. Specifically, the
correlations between videos with similar tags can be utilized to temporally
localize the activities. Towards this goal, we present W-TALC, a
Weakly-supervised Temporal Activity Localization and Classification framework
using only video-level labels. The proposed network can be divided into two
sub-networks, namely the Two-Stream based feature extractor network and a
weakly-supervised module, which we learn by optimizing two complimentary loss
functions. Qualitative and quantitative results on two challenging datasets -
Thumos14 and ActivityNet1.2, demonstrate that the proposed method is able to
detect activities at a fine granularity and achieve better performance than
current state-of-the-art methods.
Characters Detection on Namecard with faster RCNN
Authors: Weitong Zhang
Date: 2018-07-27 00:00:00
Tasks: Data Augmentation

We apply Faster R-CNN to the detection of characters in namecard, in order to
solve the problem of a small amount of data and the inbalance between different
class, we designed the data augmentation and the 'fake' data generalizer to
generate more data for the training of network. Without using data
augmentation, the average IoU in correct samples could be no less than 80% and
the mAP result of 80% was also achieved with Faster R-CNN. By applying the data
augmentation, the variance of mAP is decreased and both of the IoU and mAP
score has increased a little.
Adapting control policies from simulation to reality using a pairwise loss
Authors: Ulrich Viereck, Xingchao Peng, Kate Saenko, Robert Platt
Date: 2018-07-27 00:00:00

This paper proposes an approach to domain transfer based on a pairwise loss
function that helps transfer control policies learned in simulation onto a real
robot. We explore the idea in the context of a 'category level' manipulation
task where a control policy is learned that enables a robot to perform a mating
task involving novel objects. We explore the case where depth images are used
as the main form of sensor input. Our experimental results demonstrate that
proposed method consistently outperforms baseline methods that train only in
simulation or that combine real and simulated data in a naive way.
Perturbation Robust Representations of Topological Persistence Diagrams
Authors: Anirudh Som, Kowshik Thopalli, Karthikeyan Natesan Ramamurthy, Vinay Venkataraman, Ankita Shukla, Pavan Turaga
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: BIG-bench Machine Learning

Topological methods for data analysis present opportunities for enforcing
certain invariances of broad interest in computer vision, including view-point
in activity analysis, articulation in shape analysis, and measurement
invariance in non-linear dynamical modeling. The increasing success of these
methods is attributed to the complementary information that topology provides,
as well as availability of tools for computing topological summaries such as
persistence diagrams. However, persistence diagrams are multi-sets of points
and hence it is not straightforward to fuse them with features used for
contemporary machine learning tools like deep-nets. In this paper we present
theoretically well-grounded approaches to develop novel perturbation robust
topological representations, with the long-term view of making them amenable to
fusion with contemporary learning architectures. We term the proposed
representation as Perturbed Topological Signatures, which live on a Grassmann
manifold and hence can be efficiently used in machine learning pipelines. We
explore the use of the proposed descriptor on three applications: 3D shape
analysis, view-invariant activity analysis, and non-linear dynamical modeling.
We show favorable results in both high-level recognition performance and
time-complexity when compared to other baseline methods.
Applications of Common Entropy for Causal Inference
Authors: Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath
Date: 2018-07-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Causal Inference, valid

We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the latent variable. To efficiently compute common entropy, we propose an iterative algorithm that can be used to discover the trade-off between the entropy of the latent variable and the conditional mutual information of the observed variables. We show two applications of common entropy in causal inference: First, under the assumption that there are no low-entropy mediators, it can be used to distinguish causation from spurious correlation among almost all joint distributions on simple causal graphs with two observed variables. Second, common entropy can be used to improve constraint-based methods such as PC or FCI algorithms in the small-sample regime, where these methods are known to struggle. We propose a modification to these constraint-based methods to assess if a separating set found by these algorithms is valid using common entropy. We finally evaluate our algorithms on synthetic and real data to establish their performance.