pwc-archive/papers-with-abstracts huggingface dataset:
MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation
Authors: David Keetae Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park
Date: 2018-05-07 00:00:00
Tasks: Clustering, Image Generation, Mixture-of-Experts, MS-SSIM, SSIM

Recently, generative adversarial networks (GANs) have shown promising
performance in generating realistic images. However, they often struggle in
learning complex underlying modalities in a given dataset, resulting in
poor-quality generated images. To mitigate this problem, we present a novel
approach called mixture of experts GAN (MEGAN), an ensemble approach of
multiple generator networks. Each generator network in MEGAN specializes in
generating images with a particular subset of modalities, e.g., an image class.
Instead of incorporating a separate step of handcrafted clustering of multiple
modalities, our proposed model is trained through an end-to-end learning of
multiple generators via gating networks, which is responsible for choosing the
appropriate generator network for a given condition. We adopt the categorical
reparameterization trick for a categorical decision to be made in selecting a
generator while maintaining the flow of the gradients. We demonstrate that
individual generators learn different and salient subparts of the data and
achieve a multiscale structural similarity (MS-SSIM) score of 0.2470 for CelebA
and a competitive unsupervised inception score of 8.33 in CIFAR-10.
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network
Authors: Yunfeng Wang, Wengang Zhou, Qilin Zhang, Xiaotian Zhu, Houqiang Li
Date: 2018-05-08 00:00:00
Tasks: Action Recognition, Chunking, Optical Flow Estimation, Temporal Action Localization

Spatio-temporal contexts are crucial in understanding human actions in
videos. Recent state-of-the-art Convolutional Neural Network (ConvNet) based
action recognition systems frequently involve 3D spatio-temporal ConvNet
filters, chunking videos into fixed length clips and Long Short Term Memory
(LSTM) networks. Such architectures are designed to take advantage of both
short term and long term temporal contexts, but also requires the accumulation
of a predefined number of video frames (e.g., to construct video clips for 3D
ConvNet filters, to generate enough inputs for LSTMs). For applications that
require low-latency online predictions of fast-changing action scenes, a new
action recognition system is proposed in this paper. Termed "Weighted
Multi-Region Convolutional Neural Network" (WMR ConvNet), the proposed system
is LSTM-free, and is based on 2D ConvNet that does not require the accumulation
of video frames for 3D ConvNet filtering. Unlike early 2D ConvNets that are
based purely on RGB frames and optical flow frames, the WMR ConvNet is designed
to simultaneously capture multiple spatial and short term temporal cues (e.g.,
human poses, occurrences of objects in the background) with both the primary
region (foreground) and secondary regions (mostly background). On both the
UCF101 and HMDB51 datasets, the proposed WMR ConvNet achieves the
state-of-the-art performance among competing low-latency algorithms.
Furthermore, WMR ConvNet even outperforms the 3D ConvNet based C3D algorithm
that requires video frame accumulation. In an ablation study with the optical
flow ConvNet stream removed, the ablated WMR ConvNet nevertheless outperforms
competing algorithms.
Finding Frequent Entities in Continuous Data
Authors: Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling, Tomas Lozano-Perez
Date: 2018-05-08 00:00:00
Tasks: Clustering

In many applications that involve processing high-dimensional data, it is
important to identify a small set of entities that account for a significant
fraction of detections. Rather than formalize this as a clustering problem, in
which all detections must be grouped into hard or soft categories, we formalize
it as an instance of the frequent items or heavy hitters problem, which finds
groups of tightly clustered objects that have a high density in the feature
space. We show that the heavy hitters formulation generates solutions that are
more accurate and effective than the clustering formulation. In addition, we
present a novel online algorithm for heavy hitters, called HAC, which addresses
problems in continuous space, and demonstrate its effectiveness on real video
and household domains.
Online normalizer calculation for softmax
Authors: Maxim Milakov, Natalia Gimelshein
Date: 2018-05-08 00:00:00
Tasks: BIG-bench Machine Learning

The Softmax function is ubiquitous in machine learning, multiple previous
works suggested faster alternatives for it. In this paper we propose a way to
compute classical Softmax with fewer memory accesses and hypothesize that this
reduction in memory accesses should improve Softmax performance on actual
hardware. The benchmarks confirm this hypothesis: Softmax accelerates by up to
1.3x and Softmax+TopK combined and fused by up to 5x.
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
Authors: Akashdeep Goel, Biplab Banerjee, Aleksandra Pizurica
Date: 2017-08-04 00:00:00
Tasks: Clustering, Metric Learning, Scene Classification, Scene Recognition

We address the problem of scene classification from optical remote sensing
(RS) images based on the paradigm of hierarchical metric learning. Ideally,
supervised metric learning strategies learn a projection from a set of training
data points so as to minimize intra-class variance while maximizing inter-class
separability to the class label space. However, standard metric learning
techniques do not incorporate the class interaction information in learning the
transformation matrix, which is often considered to be a bottleneck while
dealing with fine-grained visual categories. As a remedy, we propose to
organize the classes in a hierarchical fashion by exploring their visual
similarities and subsequently learn separate distance metric transformations
for the classes present at the non-leaf nodes of the tree. We employ an
iterative max-margin clustering strategy to obtain the hierarchical
organization of the classes. Experiment results obtained on the large-scale
NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of
the proposed hierarchical metric learning based RS scene recognition strategy
in comparison to the standard approaches.
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition
Authors: Yunfeng Wang, Wengang Zhou, Qilin Zhang, Houqiang Li
Date: 2018-05-08 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Attribute, General Classification, object-detection, Object Detection, Temporal Action Localization

Visual attributes in individual video frames, such as the presence of
characteristic objects and scenes, offer substantial information for action
recognition in videos. With individual 2D video frame as input, visual
attributes extraction could be achieved effectively and efficiently with more
sophisticated convolutional neural network than current 3D CNNs with
spatio-temporal filters, thanks to fewer parameters in 2D CNNs. In this paper,
the integration of visual attributes (including detection, encoding and
classification) into multi-stream 3D CNN is proposed for action recognition in
trimmed videos, with the proposed visual Attribute-augmented 3D CNN (A3D)
framework. The visual attribute pipeline includes an object detection network,
an attributes encoding network and a classification network. Our proposed A3D
framework achieves state-of-the-art performance on both the HMDB51 and the
UCF101 datasets.
On the Conditional Logic of Simulation Models
Authors: Duligur Ibeling, Thomas Icard
Date: 2018-05-08 00:00:00

We propose analyzing conditional reasoning by appeal to a notion of
intervention on a simulation program, formalizing and subsuming a number of
approaches to conditional thinking in the recent AI literature. Our main
results include a series of axiomatizations, allowing comparison between this
framework and existing frameworks (normality-ordering models, causal structural
equation models), and a complexity result establishing NP-completeness of the
satisfiability problem. Perhaps surprisingly, some of the basic logical
principles common to all existing approaches are invalidated in our causal
simulation approach. We suggest that this additional flexibility is important
in modeling some intuitive examples.
Reasoning with Sarcasm by Reading In-between
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui, Jian Su
Date: 2018-05-08 00:00:00
Proceeding: ACL 2018 7
Tasks: Sarcasm Detection

Sarcasm is a sophisticated speech act which commonly manifests on social
communities such as Twitter and Reddit. The prevalence of sarcasm on the social
web is highly disruptive to opinion mining systems due to not only its tendency
of polarity flipping but also usage of figurative language. Sarcasm commonly
manifests with a contrastive theme either between positive-negative sentiments
or between literal-figurative scenarios. In this paper, we revisit the notion
of modeling contrast in order to reason with sarcasm. More specifically, we
propose an attention-based neural model that looks in-between instead of
across, enabling it to explicitly model contrast and incongruity. We conduct
extensive experiments on six benchmark datasets from Twitter, Reddit and the
Internet Argument Corpus. Our proposed model not only achieves state-of-the-art
performance on all datasets but also enjoys improved interpretability.
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks
Authors: Sundaresh Ram, Vicky T. Nguyen, Kirsten H. Limesand, Mert R. Sabuncu
Date: 2018-05-08 00:00:00
Tasks: Segmentation

We propose a 3D convolutional neural network to simultaneously segment and
detect cell nuclei in confocal microscopy images. Mirroring the co-dependency
of these tasks, our proposed model consists of two serial components: the first
part computes a segmentation of cell bodies, while the second module identifies
the centers of these cells. Our model is trained end-to-end from scratch on a
mouse parotid salivary gland stem cell nuclei dataset comprising 107 image
stacks from three independent cell preparations, each containing several
hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough
evaluation of both detection accuracy and segmentation quality, on two
different datasets. The results show that the proposed method provides
significantly improved detection and segmentation accuracy compared to
state-of-the-art and benchmark algorithms. Finally, we use a previously
described test-time drop-out strategy to obtain uncertainty estimates on our
predictions and validate these estimates by demonstrating that they are
strongly correlated with accuracy.
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Authors: Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, Jun Zhu
Date: 2017-12-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Adversarial Attack, Adversarial Defense, image-classification, Image Classification, Vocal Bursts Intensity Prediction

Neural networks are vulnerable to adversarial examples, which poses a threat
to their application in security sensitive systems. We propose high-level
representation guided denoiser (HGD) as a defense for image classification.
Standard denoiser suffers from the error amplification effect, in which small
residual adversarial noise is progressively amplified and leads to wrong
classifications. HGD overcomes this problem by using a loss function defined as
the difference between the target model's outputs activated by the clean image
and denoised image. Compared with ensemble adversarial training which is the
state-of-the-art defending method on large images, HGD has three advantages.
First, with HGD as a defense, the target model is more robust to either
white-box or black-box adversarial attacks. Second, HGD can be trained on a
small subset of the images and generalizes well to other images and unseen
classes. Third, HGD can be transferred to defend models other than the one
guiding it. In NIPS competition on defense against adversarial attacks, our HGD
solution won the first place and outperformed other models by a large margin.
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models
Authors: Gunwoong Park, Hyewon Park
Date: 2018-05-08 00:00:00

We introduce a new class of identifiable DAG models where the conditional distribution of each node given its parents belongs to a family of generalized hypergeometric distributions (GHD). A family of generalized hypergeometric distributions includes a lot of discrete distributions such as the binomial, Beta-binomial, negative binomial, Poisson, hyper-Poisson, and many more. We prove that if the data drawn from the new class of DAG models, one can fully identify the graph structure. We further present a reliable and polynomial-time algorithm that recovers the graph from finitely many data. We show through theoretical results and numerical experiments that our algorithm is statistically consistent in high-dimensional settings (p>n) if the indegree of the graph is bounded, and out-performs state-of-the-art DAG learning algorithms.
Fighting Accounting Fraud Through Forensic Data Analytics
Authors: Maria Jofre, Richard Gerlach
Date: 2018-05-08 00:00:00

Accounting fraud is a global concern representing a significant threat to the
financial system stability due to the resulting diminishing of the market
confidence and trust of regulatory authorities. Several tricks can be used to
commit accounting fraud, hence the need for non-static regulatory interventions
that take into account different fraudulent patterns. Accordingly, this study
aims to improve the detection of accounting fraud via the implementation of
several machine learning methods to better differentiate between fraud and
non-fraud companies, and to further assist the task of examination within the
riskier firms by evaluating relevant financial indicators. Out-of-sample
results suggest there is a great potential in detecting falsified financial
statements through statistical modelling and analysis of publicly available
accounting information. The proposed methodology can be of assistance to public
auditors and regulatory agencies as it facilitates auditing processes, and
supports more targeted and effective examinations of accounting reports.
Estimation and Tracking of AP-diameter of the Inferior Vena Cava in Ultrasound Images Using a Novel Active Circle Algorithm
Authors: Ebrahim Karami, Mohamed Shehata, Andrew Smith
Date: 2018-05-05 00:00:00

Medical research suggests that the anterior-posterior (AP)-diameter of the
inferior vena cava (IVC) and its associated temporal variation as imaged by
bedside ultrasound is useful in guiding fluid resuscitation of the
critically-ill patient. Unfortunately, indistinct edges and gaps in vessel
walls are frequently present which impede accurate estimation of the IVC
AP-diameter for both human operators and segmentation algorithms. The majority
of research involving use of the IVC to guide fluid resuscitation involves
manual measurement of the maximum and minimum AP-diameter as it varies over
time. This effort proposes using a time-varying circle fitted inside the
typically ellipsoid IVC as an efficient, consistent and novel approach to
tracking and approximating the AP-diameter even in the context of poor image
quality. In this active-circle algorithm, a novel evolution functional is
proposed and shown to be a useful tool for ultrasound image processing. The
proposed algorithm is compared with an expert manual measurement, and
state-of-the-art relevant algorithms. It is shown that the algorithm
outperforms other techniques and performs very close to manual measurement.
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction
Authors: Luowei Zhou, Nathan Louis, Jason J. Corso
Date: 2018-05-08 00:00:00
Tasks: Descriptive, Multiple Instance Learning, Object, Sentence

We study weakly-supervised video object grounding: given a video segment and
a corresponding descriptive sentence, the goal is to localize objects that are
mentioned from the sentence in the video. During training, no object bounding
boxes are available, but the set of possible objects to be grounded is known
beforehand. Existing approaches in the image domain use Multiple Instance
Learning (MIL) to ground objects by enforcing matches between visual and
semantic features. A naive extension of this approach to the video domain is to
treat the entire segment as a bag of spatial object proposals. However, an
object existing sparsely across multiple frames might not be detected
completely since successfully spotting it from one single frame would trigger a
satisfactory match. To this end, we propagate the weak supervisory signal from
the segment level to frames that likely contain the target object. For frames
that are unlikely to contain the target objects, we use an alternative penalty
loss. We also leverage the interactions among objects as a textual guide for
the grounding. We evaluate our model on the newly-collected benchmark
YouCook2-BoundingBox and show improvements over competitive baselines.
Budget-Aware Activity Detection with A Recurrent Policy Network
Authors: Behrooz Mahasseni, Xiaodong Yang, Pavlo Molchanov, Jan Kautz
Date: 2017-11-30 00:00:00
Tasks: Action Detection, Activity Detection

In this paper, we address the challenging problem of efficient temporal
activity detection in untrimmed long videos. While most recent work has focused
and advanced the detection accuracy, the inference time can take seconds to
minutes in processing each single video, which is too slow to be useful in
real-world settings. This motivates the proposed budget-aware framework, which
learns to perform activity detection by intelligently selecting a small subset
of frames according to a specified time budget. We formulate this problem as a
Markov decision process, and adopt a recurrent network to model the frame
selection policy. We derive a recurrent policy gradient based approach to
approximate the gradient of the non-decomposable and non-differentiable
objective defined in our problem. In the extensive experiments, we achieve
competitive detection accuracy, and more importantly, our approach is able to
substantially reduce computation time and detect multiple activities with only
0.35s for each untrimmed long video.
Several Tunable GMM Kernels
Authors: Ping Li
Date: 2018-05-08 00:00:00
Tasks: General Classification, Multi-class Classification

While tree methods have been popular in practice, researchers and
practitioners are also looking for simple algorithms which can reach similar
accuracy of trees. In 2010, (Ping Li UAI'10) developed the method of
"abc-robust-logitboost" and compared it with other supervised learning methods
on datasets used by the deep learning literature. In this study, we propose a
series of "tunable GMM kernels" which are simple and perform largely comparably
to tree methods on the same datasets. Note that "abc-robust-logitboost"
substantially improved the original "GDBT" in that (a) it developed a
tree-split formula based on second-order information of the derivatives of the
loss function; (b) it developed a new set of derivatives for multi-class
classification formulation.
  In the prior study in 2017, the "generalized min-max" (GMM) kernel was shown
to have good performance compared to the "radial-basis function" (RBF) kernel.
However, as demonstrated in this paper, the original GMM kernel is often not as
competitive as tree methods on the datasets used in the deep learning
literature. Since the original GMM kernel has no parameters, we propose tunable
GMM kernels by adding tuning parameters in various ways. Three basic (i.e.,
with only one parameter) GMM kernels are the "$e$GMM kernel", "$p$GMM kernel",
and "$\gamma$GMM kernel", respectively. Extensive experiments show that they
are able to produce good results for a large number of classification tasks.
Furthermore, the basic kernels can be combined to boost the performance.
Estimator of Prediction Error Based on Approximate Message Passing for Penalized Linear Regression
Authors: Ayaka Sakata
Date: 2018-02-20 00:00:00
Tasks: LEMMA, Prediction, regression

We propose an estimator of prediction error using an approximate message
passing (AMP) algorithm that can be applied to a broad range of sparse
penalties. Following Stein's lemma, the estimator of the generalized degrees of
freedom, which is a key quantity for the construction of the estimator of the
prediction error, is calculated at the AMP fixed point. The resulting form of
the AMP-based estimator does not depend on the penalty function, and its value
can be further improved by considering the correlation between predictors. The
proposed estimator is asymptotically unbiased when the components of the
predictors and response variables are independently generated according to a
Gaussian distribution. We examine the behaviour of the estimator for real data
under nonconvex sparse penalties, where Akaike's information criterion does not
correspond to an unbiased estimator of the prediction error. The model selected
by the proposed estimator is close to that which minimizes the true prediction
error.
N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
Authors: Yi Zhang, Zhengfei Wang, Guoxiong Xu, Hongshi Huang, Wenxin Li
Date: 2018-05-08 00:00:00
Tasks: Generative Adversarial Network

Foot is a vital part of human, and lots of valuable information is embedded.
Plantar pressure is one of which contains this information and it describes
human walking features. It is proved that once one has trouble with lower limb,
the distribution of plantar pressure will change to some degree. Plantar
pressure can be converted into images according to some simple standards. In
this paper, we take full advantage of these plantar pressure images for medical
usage. We present N2RPP, a generative adversarial network (GAN) based method to
rebuild plantar pressure images of anterior cruciate ligament deficiency (ACLD)
patients from low dimension features, which are extracted from an autoencoder.
Through the result of experiments, the extracted features are a useful
representation to describe and rebuild plantar pressure images. According to
N2RPP's results, we find out that there are several noteworthy differences
between normal people and patients. This can provide doctors a rough direction
of adjusting plantar pressure to a better distribution to reduce patients' sore
and pain during the rehabilitation treatment for ACLD.
Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis
Authors: Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Date: 2018-05-08 00:00:00
Proceeding: NAACL 2018 6
Tasks: Position

Election manifestos document the intentions, motives, and views of political
parties. They are often used for analysing a party's fine-grained position on a
particular issue, as well as for coarse-grained positioning of a party on the
left--right spectrum. In this paper we propose a two-stage model for
automatically performing both levels of analysis over manifestos. In the first
step we employ a hierarchical multi-task structured deep model to predict fine-
and coarse-grained positions, and in the second step we perform post-hoc
calibration of coarse-grained positions using probabilistic soft logic. We
empirically show that the proposed model outperforms state-of-art approaches at
both granularities using manifestos from twelve countries, written in ten
different languages.