pwc-archive/papers-with-abstracts huggingface dataset:
Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images
Authors: Zhi-Qi Cheng, Xiao Wu, Yang Liu, Xian-Sheng Hua
Date: 2018-04-14 00:00:00
Proceeding: CVPR 2017 7

In recent years, both online retail and video hosting service are
exponentially growing. In this paper, we explore a new cross-domain task,
Video2Shop, targeting for matching clothes appeared in videos to the exact same
items in online shops. A novel deep neural network, called AsymNet, is proposed
to explore this problem. For the image side, well-established methods are used
to detect and extract features for clothing patches with arbitrary sizes. For
the video side, deep visual features are extracted from detected object regions
in each frame, and further fed into a Long Short-Term Memory (LSTM) framework
for sequence modeling, which captures the temporal dynamics in videos. To
conduct exact matching between videos and online shopping images, LSTM hidden
states, representing the video, and image features, which represent static
object images, are jointly modeled under the similarity network with
reconfigurable deep tree structure. Moreover, an approximate training method is
proposed to achieve the efficiency when training. Extensive experiments
conducted on a large cross-domain dataset have demonstrated the effectiveness
and efficiency of the proposed AsymNet, which outperforms the state-of-the-art
methods.
On the Selection of Anchors and Targets for Video Hyperlinking
Authors: Zhi-Qi Cheng, Hao Zhang, Xiao Wu, Chong-Wah Ngo
Date: 2018-04-14 00:00:00

A problem not well understood in video hyperlinking is what qualifies a
fragment as an anchor or target. Ideally, anchors provide good starting points
for navigation, and targets supplement anchors with additional details while
not distracting users with irrelevant, false and redundant information. The
problem is not trivial for intertwining relationship between data
characteristics and user expectation. Imagine that in a large dataset, there
are clusters of fragments spreading over the feature space. The nature of each
cluster can be described by its size (implying popularity) and structure
(implying complexity). A principle way of hyperlinking can be carried out by
picking centers of clusters as anchors and from there reach out to targets
within or outside of clusters with consideration of neighborhood complexity.
The question is which fragments should be selected either as anchors or
targets, in one way to reflect the rich content of a dataset, and meanwhile to
minimize the risk of frustrating user experience. This paper provides some
insights to this question from the perspective of hubness and local intrinsic
dimensionality, which are two statistical properties in assessing the
popularity and complexity of data space. Based these properties, two novel
algorithms are proposed for low-risk automatic selection of anchors and
targets.
OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
Authors: Shiyong Ma, Zhen Zhang
Date: 2018-04-14 00:00:00

We developed OmicsMapNet approach to take advantage of existing deep leaning frameworks to analyze high-dimensional omics data as 2-dimensional images. The omics data of individual samples were first rearranged into 2D images in which molecular features related in functions, ontologies, or other relationships were organized in spatially adjacent and patterned locations. Deep learning neural networks were trained to classify the images. Molecular features informative of classes of different phenotypes were subsequently identified. As an example, we used the KEGG BRITE database to rearrange RNA-Seq expression data of TCGA diffuse glioma samples as treemaps to capture the functional hierarchical structure of genes in 2D images. Deep Convolutional Neural Networks (CNN) were derived using tools from TensorFlow to learn the grade of TCGA LGG and GBM samples with relatively high accuracy. The most contributory features in the trained CNN were confirmed in pathway analysis for their plausible functional involvement.
Predicting Cyber Events by Leveraging Hacker Sentiment
Authors: Ashok Deb, Kristina Lerman, Emilio Ferrara
Date: 2018-04-14 00:00:00
Tasks: Sentiment Analysis, Time Series, Time Series Analysis

Recent high-profile cyber attacks exemplify why organizations need better
cyber defenses. Cyber threats are hard to accurately predict because attackers
usually try to mask their traces. However, they often discuss exploits and
techniques on hacking forums. The community behavior of the hackers may provide
insights into groups' collective malicious activity. We propose a novel
approach to predict cyber events using sentiment analysis. We test our approach
using cyber attack data from 2 major business organizations. We consider 3
types of events: malicious software installation, malicious destination visits,
and malicious emails that surpassed the target organizations' defenses. We
construct predictive signals by applying sentiment analysis on hacker forum
posts to better understand hacker behavior. We analyze over 400K posts
generated between January 2016 and January 2018 on over 100 hacking forums both
on surface and Dark Web. We find that some forums have significantly more
predictive power than others. Sentiment-based models that leverage specific
forums can outperform state-of-the-art deep learning and time-series models on
forecasting cyber attacks weeks ahead of the events.
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
Authors: Felix M. Riese, Sina Keller
Date: 2018-04-14 00:00:00
Tasks: GPR, regression, Soil moisture estimation

In this contribution, we investigate the potential of hyperspectral data
combined with either simulated ground penetrating radar (GPR) or simulated
(sensor-like) soil-moisture data to estimate soil moisture. We propose two
simulation approaches to extend a given multi-sensor dataset which contains
sparse GPR data. In the first approach, simulated GPR data is generated either
by an interpolation along the time axis or by a machine learning model. The
second approach includes the simulation of soil-moisture along the GPR profile.
The soil-moisture estimation is improved significantly by the fusion of
hyperspectral and GPR data. In contrast, the combination of simulated,
sensor-like soil-moisture values and hyperspectral data achieves the worst
regression performance. In conclusion, the estimation of soil moisture with
hyperspectral and GPR data engages further investigations.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Authors: Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, Kevin Chan
Date: 2018-04-14 00:00:00
Tasks: BIG-bench Machine Learning, Edge-computing, Federated Learning

Emerging technologies and applications including Internet of Things (IoT),
social networking, and crowd-sourcing generate large amounts of data at the
network edge. Machine learning models are often built from the collected data,
to enable the detection, classification, and prediction of future events. Due
to bandwidth, storage, and privacy concerns, it is often impractical to send
all the data to a centralized location. In this paper, we consider the problem
of learning model parameters from data distributed across multiple edge nodes,
without sending raw data to a centralized place. Our focus is on a generic
class of machine learning models that are trained using gradient-descent based
approaches. We analyze the convergence bound of distributed gradient descent
from a theoretical point of view, based on which we propose a control algorithm
that determines the best trade-off between local update and global parameter
aggregation to minimize the loss function under a given resource budget. The
performance of the proposed algorithm is evaluated via extensive experiments
with real datasets, both on a networked prototype system and in a larger-scale
simulated environment. The experimentation results show that our proposed
approach performs near to the optimum with various machine learning models and
different data distributions.
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
Authors: Wenqi Xian, Patsorn Sangkloy, Varun Agrawal, Amit Raj, Jingwan Lu, Chen Fang, Fisher Yu, James Hays
Date: 2017-06-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation, Texture Synthesis

In this paper, we investigate deep image synthesis guided by sketch, color,
and texture. Previous image synthesis methods can be controlled by sketch and
color strokes but we are the first to examine texture control. We allow a user
to place a texture patch on a sketch at arbitrary locations and scales to
control the desired output texture. Our generative network learns to synthesize
objects consistent with these texture suggestions. To achieve this, we develop
a local texture loss in addition to adversarial and content loss to train the
generative network. We conduct experiments using sketches generated from real
images and textures sampled from a separate texture database and results show
that our proposed algorithm is able to generate plausible images that are
faithful to user controls. Ablation studies show that our proposed pipeline can
generate more realistic images than adapting existing methods directly.
Low-Precision Floating-Point Schemes for Neural Network Training
Authors: Marc Ortiz, Adrián Cristal, Eduard Ayguadé, Marc Casas
Date: 2018-04-14 00:00:00

The use of low-precision fixed-point arithmetic along with stochastic
rounding has been proposed as a promising alternative to the commonly used
32-bit floating point arithmetic to enhance training neural networks training
in terms of performance and energy efficiency. In the first part of this paper,
the behaviour of the 12-bit fixed-point arithmetic when training a
convolutional neural network with the CIFAR-10 dataset is analysed, showing
that such arithmetic is not the most appropriate for the training phase. After
that, the paper presents and evaluates, under the same conditions, alternative
low-precision arithmetics, starting with the 12-bit floating-point arithmetic.
These two representations are then leveraged using local scaling in order to
increase accuracy and get closer to the baseline 32-bit floating-point
arithmetic. Finally, the paper introduces a simplified model in which both the
outputs and the gradients of the neural networks are constrained to
power-of-two values, just using 7 bits for their representation. The evaluation
demonstrates a minimal loss in accuracy for the proposed Power-of-Two neural
network, avoiding the use of multiplications and divisions and thereby,
significantly reducing the training time as well as the energy consumption and
memory requirements during the training and inference phases.
Deep Sets
Authors: Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola
Date: 2017-03-10 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Anomaly Detection, Outlier Detection, Point Cloud Classification

We study the problem of designing models for machine learning tasks defined
on \emph{sets}. In contrast to traditional approach of operating on fixed
dimensional vectors, we consider objective functions defined on sets that are
invariant to permutations. Such problems are widespread, ranging from
estimation of population statistics \cite{poczos13aistats}, to anomaly
detection in piezometer data of embankment dams \cite{Jung15Exploration}, to
cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem
characterizes the permutation invariant functions and provides a family of
functions to which any permutation invariant objective function must belong.
This family of functions has a special structure which enables us to design a
deep network architecture that can operate on sets and which can be deployed on
a variety of scenarios including both unsupervised and supervised learning
tasks. We also derive the necessary and sufficient conditions for permutation
equivariance in deep models. We demonstrate the applicability of our method on
population statistic estimation, point cloud classification, set expansion, and
outlier detection.
Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings
Authors: Joshua Coates, Danushka Bollegala
Date: 2018-04-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Word Embeddings

Creating accurate meta-embeddings from pre-trained source embeddings has
received attention lately. Methods based on global and locally-linear
transformation and concatenation have shown to produce accurate
meta-embeddings. In this paper, we show that the arithmetic mean of two
distinct word embedding sets yields a performant meta-embedding that is
comparable or better than more complex meta-embedding learning methods. The
result seems counter-intuitive given that vector spaces in different source
embeddings are not comparable and cannot be simply averaged. We give insight
into why averaging can still produce accurate meta-embedding despite the
incomparability of the source vector spaces.
Physics-driven Fire Modeling from Multi-view Images
Authors: Garoe Dorta, Luca Benedetti, Dmitry Kit, Yong-Liang Yang
Date: 2018-04-14 00:00:00
Tasks: Physical Simulations, valid

Fire effects are widely used in various computer graphics applications such
as visual effects and video games. Modeling the shape and appearance of fire
phenomenon is challenging as the underlying effects are driven by complex laws
of physics. State-of-the-art fire modeling techniques rely on sophisticated
physical simulations which require intensive parameter tuning, or use
simplifications which produce physically invalid results. In this paper, we
present a novel method of reconstructing physically valid fire models from
multi-view stereo images. Our method, for the first time, provides plausible
estimation of physical properties (e.g., temperature, density) of a fire volume
using RGB cameras. This allows for a number of novel phenomena such as global
fire illumination effects. The effectiveness and usefulness of our method are
tested by generating fire models from a variety of input data, and applying the
reconstructed fire models for realistic illumination of virtual scenes.
ClassiNet -- Predicting Missing Features for Short-Text Classification
Authors: Danushka Bollegala, Vincent Atanasov, Takanori Maehara, Ken-ichi Kawarabayashi
Date: 2018-04-14 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

The fundamental problem in short-text classification is \emph{feature
sparseness} -- the lack of feature overlap between a trained model and a test
instance to be classified. We propose \emph{ClassiNet} -- a network of
classifiers trained for predicting missing features in a given instance, to
overcome the feature sparseness problem. Using a set of unlabeled training
instances, we first learn binary classifiers as feature predictors for
predicting whether a particular feature occurs in a given instance. Next, each
feature predictor is represented as a vertex $v_i$ in the ClassiNet where a
one-to-one correspondence exists between feature predictors and vertices. The
weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex
$v_j$ represents the conditional probability that given $v_i$ exists in an
instance, $v_j$ also exists in the same instance. We show that ClassiNets
generalize word co-occurrence graphs by considering implicit co-occurrences
between features. We extract numerous features from the trained ClassiNet to
overcome feature sparseness. In particular, for a given instance $\vec{x}$, we
find similar features from ClassiNet that did not appear in $\vec{x}$, and
append those features in the representation of $\vec{x}$. Moreover, we propose
a method based on graph propagation to find features that are indirectly
related to a given short-text. We evaluate ClassiNets on several benchmark
datasets for short-text classification. Our experimental results show that by
using ClassiNet, we can statistically significantly improve the accuracy in
short-text classification tasks, without having to use any external resources
such as thesauri for finding related features.
Distribution-based Prediction of the Degree of Grammaticalization for German Prepositions
Authors: Dominik Schlechtweg, Sabine Schulte im Walde
Date: 2018-04-14 00:00:00

We test the hypothesis that the degree of grammaticalization of German
prepositions correlates with their corpus-based contextual dispersion measured
by word entropy. We find that there is indeed a moderate correlation for
entropy, but a stronger correlation for frequency and number of context types.
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks
Authors: Sina Sajadmanesh, Sogol Bazargani, Jiawei Zhang, Hamid R. Rabiee
Date: 2017-09-30 00:00:00
Tasks: Link Prediction, Prediction

Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train NP-GLM and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of NP-GLM in solving continuous-time relationship prediction problem vis-a-vis competitive baselines
"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition
Authors: Debanjan Ghosh, Smaranda Muresan
Date: 2018-04-14 00:00:00
Tasks: General Classification, TAG

Conversations in social media often contain the use of irony or sarcasm, when
the users say the opposite of what they really mean. Irony markers are the
meta-communicative clues that inform the reader that an utterance is ironic. We
propose a thorough analysis of theoretically grounded irony markers in two
social media platforms: $Twitter$ and $Reddit$. Classification and frequency
analysis show that for $Twitter$, typographic markers such as emoticons and
emojis are the most discriminative markers to recognize ironic utterances,
while for $Reddit$ the morphological markers (e.g., interjections, tag
questions) are the most discriminative.
An interpretable LSTM neural network for autoregressive exogenous model
Authors: Tian Guo, Tao Lin, Yao Lu
Date: 2018-04-14 00:00:00
Tasks: Decoder, Time Series, Time Series Analysis

In this paper, we propose an interpretable LSTM recurrent neural network,
i.e., multi-variable LSTM for time series with exogenous variables. Currently,
widely used attention mechanism in recurrent neural networks mostly focuses on
the temporal aspect of data and falls short of characterizing variable
importance. To this end, our multi-variable LSTM equipped with tensorized
hidden states is developed to learn variable specific representations, which
give rise to both temporal and variable level attention. Preliminary
experiments demonstrate comparable prediction performance of multi-variable
LSTM w.r.t. encoder-decoder based baselines. More interestingly, variable
importance in real datasets characterized by the variable attention is highly
in line with that determined by statistical Granger causality test, which
exhibits the prospect of multi-variable LSTM as a simple and uniform end-to-end
framework for both forecasting and knowledge discovery.
Tensor SVD: Statistical and Computational Limits
Authors: Anru Zhang, Dong Xia
Date: 2017-03-08 00:00:00

In this paper, we propose a general framework for tensor singular value decomposition (tensor SVD), which focuses on the methodology and theory for extracting the hidden low-rank structure from high-dimensional tensor data. Comprehensive results are developed on both the statistical and computational limits for tensor SVD. This problem exhibits three different phases according to the signal-to-noise ratio (SNR). In particular, with strong SNR, we show that the classical higher-order orthogonal iteration achieves the minimax optimal rate of convergence in estimation; with weak SNR, the information-theoretical lower bound implies that it is impossible to have consistent estimation in general; with moderate SNR, we show that the non-convex maximum likelihood estimation provides optimal solution, but with NP-hard computational cost; moreover, under the hardness hypothesis of hypergraphic planted clique detection, there are no polynomial-time algorithms performing consistently in general.
Creating Capsule Wardrobes from Fashion Images
Authors: Wei-Lin Hsiao, Kristen Grauman
Date: 2017-12-07 00:00:00
Proceeding: CVPR 2018 6

We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Authors: Yan Zheng, Jianye Hao, Zongzhang Zhang
Date: 2018-02-23 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, multiagent deep reinforcement learning (DRL) has received
increasingly wide attention. Existing multiagent DRL algorithms are inefficient
when facing with the non-stationarity due to agents update their policies
simultaneously in stochastic cooperative environments. This paper extends the
recently proposed weighted double estimator to the multiagent domain and
propose a multiagent DRL framework, named weighted double deep Q-network
(WDDQN). By utilizing the weighted double estimator and the deep neural
network, WDDQN can not only reduce the bias effectively but also be extended to
scenarios with raw visual inputs. To achieve efficient cooperation in the
multiagent domain, we introduce the lenient reward network and the scheduled
replay strategy. Experiments show that the WDDQN outperforms the existing DRL
and multiaent DRL algorithms, i.e., double DQN and lenient Q-learning, in terms
of the average reward and the convergence rate in stochastic cooperative
environments.