pwc-archive/papers-with-abstracts huggingface dataset:
Deep Style Match for Complementary Recommendation
Authors: Kui Zhao, Xia Hu, Jiajun Bu, Can Wang
Date: 2017-08-26 00:00:00
Tasks: Common Sense Reasoning, Feature Engineering

Humans develop a common sense of style compatibility between items based on
their attributes. We seek to automatically answer questions like "Does this
shirt go well with that pair of jeans?" In order to answer these kinds of
questions, we attempt to model human sense of style compatibility in this
paper. The basic assumption of our approach is that most of the important
attributes for a product in an online store are included in its title
description. Therefore it is feasible to learn style compatibility from these
descriptions. We design a Siamese Convolutional Neural Network architecture and
feed it with title pairs of items, which are either compatible or incompatible.
Those pairs will be mapped from the original space of symbolic words into some
embedded style space. Our approach takes only words as the input with few
preprocessing and there is no laborious and expensive feature engineering.
3D Binary Signatures
Authors: Siddharth Srivastava, Brejesh lall
Date: 2017-08-26 00:00:00

In this paper, we propose a novel binary descriptor for 3D point clouds. The
proposed descriptor termed as 3D Binary Signature (3DBS) is motivated from the
matching efficiency of the binary descriptors for 2D images. 3DBS describes
keypoints from point clouds with a binary vector resulting in extremely fast
matching. The method uses keypoints from standard keypoint detectors. The
descriptor is built by constructing a Local Reference Frame and aligning a
local surface patch accordingly. The local surface patch constitutes of
identifying nearest neighbours based upon an angular constraint among them. The
points are ordered with respect to the distance from the keypoints. The normals
of the ordered pairs of these keypoints are projected on the axes and the
relative magnitude is used to assign a binary digit. The vector thus
constituted is used as a signature for representing the keypoints. The matching
is done by using hamming distance. We show that 3DBS outperforms state of the
art descriptors on various evaluation metrics.
Robust Stereo Feature Descriptor for Visual Odometry
Authors: Ehsan Shojaedini, Reza Safabakhsh
Date: 2017-08-26 00:00:00
Tasks: Visual Odometry

In this paper, we propose a simple way to utilize stereo camera data to
improve feature descriptors. Computer vision algorithms that use a stereo
camera require some calculations of 3D information. We leverage this
pre-calculated information to improve feature descriptor algorithms. We use the
3D feature information to estimate the scale of each feature. This way, each
feature descriptor will be more robust to scale change without significant
computations. In addition, we use stereo images to construct the descriptor
vector. The Scale-Invariant Feature Transform (SIFT) and Fast Retina Keypoint
(FREAK) descriptors are used to evaluate the proposed method. The scale
normalization technique in feature tracking test improves the standard SIFT by
8.75% and improves the standard FREAK by 28.65%. Using the proposed stereo
feature descriptor, a visual odometry algorithm is designed and tested on the
KITTI dataset. The stereo FREAK descriptor raises the number of inlier matches
by 19% and consequently improves the accuracy of visual odometry by 23%.
Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance
Authors: Hidetoshi Furukawa
Date: 2017-08-26 00:00:00
Tasks: Data Augmentation, General Classification, Translation

This report deals with translation invariance of convolutional neural
networks (CNNs) for automatic target recognition (ATR) from synthetic aperture
radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR
ATR represents the robustness against misalignment of target chips extracted
from SAR images. To understand the translation invariance of the CNNs, we
trained CNNs which classify the target chips from the MSTAR into the ten
classes under the condition of with and without data augmentation, and then
visualized the translation invariance of the CNNs. According to our results,
even if we use a deep residual network, the translation invariance of the CNN
without data augmentation using the aligned images such as the MSTAR target
chips is not so large. A more important factor of translation invariance is the
use of augmented training data. Furthermore, our CNN using augmented training
data achieved a state-of-the-art classification accuracy of 99.6%. These
results show an importance of domain-specific data augmentation.
Dealing with Range Anxiety in Mean Estimation via Statistical Queries
Authors: Vitaly Feldman
Date: 2016-11-20 00:00:00

We give algorithms for estimating the expectation of a given real-valued
function $\phi:X\to {\bf R}$ on a sample drawn randomly from some unknown
distribution $D$ over domain $X$, namely ${\bf E}_{{\bf x}\sim D}[\phi({\bf
x})]$. Our algorithms work in two well-studied models of restricted access to
data samples. The first one is the statistical query (SQ) model in which an
algorithm has access to an SQ oracle for the input distribution $D$ over $X$
instead of i.i.d. samples from $D$. Given a query function $\phi:X \to [0,1]$,
the oracle returns an estimate of ${\bf E}_{{\bf x}\sim D}[\phi({\bf x})]$
within some tolerance $\tau$. The second, is a model in which only a single bit
is communicated from each sample. In both of these models the error obtained
using a naive implementation would scale polynomially with the range of the
random variable $\phi({\bf x})$ (which might even be infinite). In contrast,
without restrictions on access to data the expected error scales with the
standard deviation of $\phi({\bf x})$. Here we give a simple algorithm whose
error scales linearly in standard deviation of $\phi({\bf x})$ and
logarithmically with an upper bound on the second moment of $\phi({\bf x})$.
  As corollaries, we obtain algorithms for high dimensional mean estimation and
stochastic convex optimization in these models that work in more general
settings than previously known solutions.
An Adaptive Cluster-based Filtering Framework for Speckle Reduction of OCT Skin Images
Authors: Elaheh Rashedi, Saba Adabi, Darius Mehregan, Silvia Conforto, Xue-wen Chen
Date: 2017-07-31 00:00:00
Tasks: Clustering

Optical coherence tomography (OCT) has become a favorable device in the
Dermatology discipline due to its moderate resolution and penetration depth.
OCT images however contain a grainy pattern, called speckle, due to the use of
a broadband source in the configuration of OCT. So far, a variety of filtering
(de-speckling) techniques is introduced to reduce speckle in OCT images. Most
of these methods are generic and can be applied to OCT images of different
tissues. The ambition of this work is to provide a de-speckling framework
specialized for filtering skin tissues for the community to utilize, adapt or
build upon. In this paper, we present an adaptive cluster-based filtering
framework, optimized for speckle reduction of OCT skin images. In this
framework, by considering the layered structure of skin, first the OCT skin
images are segmented into differentiable layers utilizing clustering
algorithms, and then each cluster is de-speckled individually using adaptive
filtering techniques. In this study, hierarchical clustering algorithm and
adaptive Wiener filtering technique are utilized to develop the framework. The
proposed method is tested on optical solid phantoms with predetermined optical
properties. The method is also tested on healthy human skin images. The results
show that the proposed cluster-based filtering method can effectively reduce
the speckle and increase the signal-to-noise ratio and contrast while
preserving the edges in the image. The proposed cluster-based filtering
framework enables researchers to develop unsupervised learning solutions for
de-speckling OCT skin images using adaptive filtering methods, or extend the
framework to new applications.
Multidimensional Scaling on Multiple Input Distance Matrices
Authors: Song Bai, Xiang Bai, Longin Jan Latecki, Qi Tian
Date: 2016-05-01 00:00:00

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial
representations for data points, given the pairwise distances between them.
However, in recent years, data are usually collected from diverse sources or
have multiple heterogeneous representations. How to do multidimensional scaling
on multiple input distance matrices is still unsolved to our best knowledge. In
this paper, we first define this new task formally. Then, we propose a new
algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering
each input distance matrix as one view. Our algorithm is able to learn the
weights of views (i.e., distance matrices) automatically by exploring the
consensus information and complementary nature of views. Experimental results
on synthetic as well as real datasets demonstrate the effectiveness of MVMDS.
We hope that our work encourages a wider consideration in many domains where
MDS is needed.
Network Essence: PageRank Completion and Centrality-Conforming Markov Chains
Authors: Shang-Hua Teng
Date: 2017-08-25 00:00:00

Ji\v{r}\'i Matou\v{s}ek (1963-2015) had many breakthrough contributions in
mathematics and algorithm design. His milestone results are not only profound
but also elegant. By going beyond the original objects --- such as Euclidean
spaces or linear programs --- Jirka found the essence of the challenging
mathematical/algorithmic problems as well as beautiful solutions that were
natural to him, but were surprising discoveries to the field.
  In this short exploration article, I will first share with readers my initial
encounter with Jirka and discuss one of his fundamental geometric results from
the early 1990s. In the age of social and information networks, I will then
turn the discussion from geometric structures to network structures, attempting
to take a humble step towards the holy grail of network science, that is to
understand the network essence that underlies the observed
sparse-and-multifaceted network data. I will discuss a simple result which
summarizes some basic algebraic properties of personalized PageRank matrices.
Unlike the traditional transitive closure of binary relations, the personalized
PageRank matrices take "accumulated Markovian closure" of network data. Some of
these algebraic properties are known in various contexts. But I hope featuring
them together in a broader context will help to illustrate the desirable
properties of this Markovian completion of networks, and motivate systematic
developments of a network theory for understanding vast and ubiquitous
multifaceted network data.
Independently Controllable Factors
Authors: Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio
Date: 2017-08-03 00:00:00
Tasks: Open-Ended Question Answering

It has been postulated that a good representation is one that disentangles
the underlying explanatory factors of variation. However, it remains an open
question what kind of training framework could potentially achieve that.
Whereas most previous work focuses on the static setting (e.g., with images),
we postulate that some of the causal factors could be discovered if the learner
is allowed to interact with its environment. The agent can experiment with
different actions and observe their effects. More specifically, we hypothesize
that some of these factors correspond to aspects of the environment which are
independently controllable, i.e., that there exists a policy and a learnable
feature for each such aspect of the environment, such that this policy can
yield changes in that feature with minimal changes to other features that
explain the statistical variations in the observed data. We propose a specific
objective function to find such factors and verify experimentally that it can
indeed disentangle independently controllable aspects of the environment
without any extrinsic reward signal.
Nationality Classification Using Name Embeddings
Authors: Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena
Date: 2017-08-25 00:00:00
Tasks: Classification, General Classification

Nationality identification unlocks important demographic information, with
many applications in biomedical and sociological research. Existing name-based
nationality classifiers use name substrings as features and are trained on
small, unrepresentative sets of labeled names, typically extracted from
Wikipedia. As a result, these methods achieve limited performance and cannot
support fine-grained classification.
  We exploit the phenomena of homophily in communication patterns to learn name
embeddings, a new representation that encodes gender, ethnicity, and
nationality which is readily applicable to building classifiers and other
systems. Through our analysis of 57M contact lists from a major Internet
company, we are able to design a fine-grained nationality classifier covering
39 groups representing over 90% of the world population. In an evaluation
against other published systems over 13 common classes, our F1 score (0.795) is
substantial better than our closest competitor Ethnea (0.580). To the best of
our knowledge, this is the most accurate, fine-grained nationality classifier
available.
  As a social media application, we apply our classifiers to the followers of
major Twitter celebrities over six different domains. We demonstrate stark
differences in the ethnicities of the followers of Trump and Obama, and in the
sports and entertainments favored by different groups. Finally, we identify an
anomalous political figure whose presumably inflated following appears largely
incapable of reading the language he posts in.
Batch-Based Activity Recognition from Egocentric Photo-Streams
Authors: Alejandro Cartas, Mariella Dimiccoli, Petia Radeva
Date: 2017-08-25 00:00:00
Tasks: Activity Recognition, Optical Flow Estimation

Activity recognition from long unstructured egocentric photo-streams has
several applications in assistive technology such as health monitoring and
frailty detection, just to name a few. However, one of its main technical
challenges is to deal with the low frame rate of wearable photo-cameras, which
causes abrupt appearance changes between consecutive frames. In consequence,
important discriminatory low-level features from motion such as optical flow
cannot be estimated. In this paper, we present a batch-driven approach for
training a deep learning architecture that strongly rely on Long short-term
units to tackle this problem. We propose two different implementations of the
same approach that process a photo-stream sequence using batches of fixed size
with the goal of capturing the temporal evolution of high-level features. The
main difference between these implementations is that one explicitly models
consecutive batches by overlapping them. Experimental results over a public
dataset acquired by three users demonstrate the validity of the proposed
architectures to exploit the temporal evolution of convolutional features over
time without relying on event boundaries.
RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection
Authors: Joshua J. Engelsma, Kai Cao, Anil K. Jain
Date: 2017-08-25 00:00:00

We present the design and prototype of an open source, optical fingerprint
reader, called RaspiReader, using ubiquitous components. RaspiReader, a
low-cost and easy to assemble reader, provides the fingerprint research
community a seamless and simple method for gaining more control over the
sensing component of fingerprint recognition systems. In particular, we posit
that this versatile fingerprint reader will encourage researchers to explore
novel spoof detection methods that integrate both hardware and software.
RaspiReader's hardware is customized with two cameras for fingerprint
acquisition with one camera providing high contrast, frustrated total internal
reflection (FTIR) images, and the other camera outputting direct images. Using
both of these image streams, we extract complementary information which, when
fused together, results in highly discriminative features for fingerprint spoof
(presentation attack) detection. Our experimental results demonstrate a marked
improvement over previous spoof detection methods which rely only on FTIR
images provided by COTS optical readers. Finally, fingerprint matching
experiments between images acquired from the FTIR output of the RaspiReader and
images acquired from a COTS fingerprint reader verify the interoperability of
the RaspiReader with existing COTS optical readers.
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
Authors: Rui Wang, Martin Schwörer, Daniel Cremers
Date: 2017-08-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Optical Flow Estimation, Visual Odometry

We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for
highly accurate real-time visual odometry estimation of large-scale
environments from stereo cameras. It jointly optimizes for all the model
parameters within the active window, including the intrinsic/extrinsic camera
parameters of all keyframes and the depth values of all selected pixels. In
particular, we propose a novel approach to integrate constraints from static
stereo into the bundle adjustment pipeline of temporal multi-view stereo.
Real-time optimization is realized by sampling pixels uniformly from image
regions with sufficient intensity gradient. Fixed-baseline stereo resolves
scale drift. It also reduces the sensitivities to large optical flow and to
rolling shutter effect which are known shortcomings of direct image alignment
methods. Quantitative evaluation demonstrates that the proposed Stereo DSO
outperforms existing state-of-the-art visual odometry methods both in terms of
tracking accuracy and robustness. Moreover, our method delivers a more precise
metric 3D reconstruction than previous dense/semi-dense direct approaches while
providing a higher reconstruction density than feature-based methods.
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Authors: Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li
Date: 2017-08-25 00:00:00
Tasks: Graph Learning, Intrusion Detection, Transfer Learning

Dependency graph, as a heterogeneous graph representing the intrinsic
relationships between different pairs of system entities, is essential to many
data analysis applications, such as root cause diagnosis, intrusion detection,
etc. Given a well-trained dependency graph from a source domain and an immature
dependency graph from a target domain, how can we extract the entity and
dependency knowledge from the source to enhance the target? One way is to
directly apply a mature dependency graph learned from a source domain to the
target domain. But due to the domain variety problem, directly using the source
dependency graph often can not achieve good performance. Traditional transfer
learning methods mainly focus on numerical data and are not applicable.
  In this paper, we propose ACRET, a knowledge transfer based model for
accelerating dependency graph learning from heterogeneous categorical event
streams. In particular, we first propose an entity estimation model to filter
out irrelevant entities from the source domain based on entity embedding and
manifold learning. Only the entities with statistically high correlations are
transferred to the target domain. On the surviving entities, we propose a
dependency construction model for constructing the unbiased dependency
relationships by solving a two-constraint optimization problem. The
experimental results on synthetic and real-world datasets demonstrate the
effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise
security system for intrusion detection. Our method is able to achieve superior
detection performance at least 20 days lead lag time in advance with more than
70% accuracy.
$k$-Nearest Neighbor Augmented Neural Networks for Text Classification
Authors: Zhiguo Wang, Wael Hamza, Linfeng Song
Date: 2017-08-25 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Transfer Learning

In recent years, many deep-learning based models are proposed for text
classification. This kind of models well fits the training set from the
statistical point of view. However, it lacks the capacity of utilizing
instance-level information from individual instances in the training set. In
this work, we propose to enhance neural network models by allowing them to
leverage information from $k$-nearest neighbor (kNN) of the input text. Our
model employs a neural network that encodes texts into text embeddings.
Moreover, we also utilize $k$-nearest neighbor of the input text as an external
memory, and utilize it to capture instance-level information from the training
set. The final prediction is made based on features from both the neural
network encoder and the kNN memory. Experimental results on several standard
benchmark datasets show that our model outperforms the baseline model on all
the datasets, and it even beats a very deep neural network model (with 29
layers) in several datasets. Our model also shows superior performance when
training instances are scarce, and when the training set is severely
unbalanced. Our model also leverages techniques such as semi-supervised
training and transfer learning quite well.
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
Authors: Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing
Date: 2017-03-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Clustering, Representation Learning, Variational Inference

The recently developed variational autoencoders (VAEs) have proved to be an
effective confluence of the rich representational power of neural networks with
Bayesian methods. However, most work on VAEs use a rather simple prior over the
latent variables such as standard normal distribution, thereby restricting its
applications to relatively simple phenomena. In this work, we propose
hierarchical nonparametric variational autoencoders, which combines
tree-structured Bayesian nonparametric priors with VAEs, to enable infinite
flexibility of the latent representation space. Both the neural parameters and
Bayesian priors are learned jointly using tailored variational inference. The
resulting model induces a hierarchical structure of latent semantic concepts
underlying the data corpus, and infers accurate representations of data
instances. We apply our model in video representation learning. Our method is
able to discover highly interpretable activity hierarchies, and obtain improved
clustering accuracy and generalization capacity based on the learned rich
representations.
Multi-task Self-Supervised Visual Learning
Authors: Carl Doersch, Andrew Zisserman
Date: 2017-08-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: Depth Estimation, Depth Prediction, General Classification, Self-Supervised Image Classification

We investigate methods for combining multiple self-supervised tasks--i.e.,
supervised tasks where data can be collected without manual labeling--in order
to train a single visual representation. First, we provide an apples-to-apples
comparison of four different self-supervised tasks using the very deep
ResNet-101 architecture. We then combine tasks to jointly train a network. We
also explore lasso regularization to encourage the network to factorize the
information in its representation, and methods for "harmonizing" network inputs
in order to learn a more unified representation. We evaluate all methods on
ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our
results show that deeper networks work better, and that combining tasks--even
via a naive multi-head architecture--always improves performance. Our best
joint network nearly matches the PASCAL performance of a model pre-trained on
ImageNet classification, and matches the ImageNet network on NYU depth
prediction.