pwc-archive/papers-with-abstracts huggingface dataset:
Kernel functions based on triplet comparisons
Authors: Matthäus Kleindessner, Ulrike Von Luxburg
Date: 2016-07-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Object, Triplet

Given only information in the form of similarity triplets "Object A is more
similar to object B than to object C" about a data set, we propose two ways of
defining a kernel function on the data set. While previous approaches construct
a low-dimensional Euclidean embedding of the data set that reflects the given
similarity triplets, we aim at defining kernel functions that correspond to
high-dimensional embeddings. These kernel functions can subsequently be used to
apply any kernel method to the data set.
Multilinear Class-Specific Discriminant Analysis
Authors: Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Date: 2017-10-29 00:00:00
Tasks: Stock Price Prediction

There has been a great effort to transfer linear discriminant techniques that
operate on vector data to high-order data, generally referred to as Multilinear
Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing
the inter-class variances to intra-class variances defined on tensor data
representations. However, there has not been any attempt to employ
class-specific discrimination criteria for the tensor data. In this paper, we
propose a multilinear subspace learning technique suitable for applications
requiring class-specific tensor models. The method maximizes the discrimination
of each individual class in the feature space while retains the spatial
structure of the input. We evaluate the efficiency of the proposed method on
two problems, i.e. facial image analysis and stock price prediction based on
limit order book data.
Kernel Graph Convolutional Neural Networks
Authors: Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
Date: 2017-10-29 00:00:00
Tasks: General Classification, Graph Classification

Graph kernels have been successfully applied to many graph classification
problems. Typically, a kernel is first designed, and then an SVM classifier is
trained based on the features defined implicitly by this kernel. This two-stage
approach decouples data representation from learning, which is suboptimal. On
the other hand, Convolutional Neural Networks (CNNs) have the capability to
learn their own features directly from the raw data during training.
Unfortunately, they cannot handle irregular data such as graphs. We address
this challenge by using graph kernels to embed meaningful local neighborhoods
of the graphs in a continuous vector space. A set of filters is then convolved
with these patches, pooled, and the output is then passed to a feedforward
network. With limited parameter tuning, our approach outperforms strong
baselines on 7 out of 10 benchmark datasets.
High-Precision Localization Using Ground Texture
Authors: Linguang Zhang, Adam Finkelstein, Szymon Rusinkiewicz
Date: 2017-10-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces.
Regularization for Deep Learning: A Taxonomy
Authors: Jan Kukačka, Vladimir Golkov, Daniel Cremers
Date: 2017-10-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning

Regularization is one of the crucial ingredients of deep learning, yet the
term regularization has various definitions, and regularization methods are
often studied separately from each other. In our work we present a systematic,
unifying taxonomy to categorize existing methods. We distinguish methods that
affect data, network architectures, error terms, regularization terms, and
optimization procedures. We do not provide all details about the listed
methods; instead, we present an overview of how the methods can be sorted into
meaningful categories and sub-categories. This helps revealing links and
fundamental similarities between them. Finally, we include practical
recommendations both for users and for developers of new regularization
methods.
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Authors: Devinder Kumar, Graham W. Taylor, Alexander Wong
Date: 2017-10-29 00:00:00
Tasks: Decision Making, Diabetic Retinopathy Grading, Diagnostic

Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of medical imaging data. One of the main limitations with
current CAD approaches is that it is very difficult to gain insight or
rationale as to how decisions are made, thus limiting their utility to
clinicians. Methods: In this study, we propose CLEAR-DR, a novel interpretable
CAD system based on the notion of CLass-Enhanced Attentive Response Discovery
Radiomics for the purpose of clinical decision support for diabetic
retinopathy. Results: In addition to disease grading via the discovered deep
radiomic sequencer, the CLEAR-DR system also produces a visual interpretation
of the decision-making process to provide better insight and understanding into
the decision-making process of the system. Conclusion: We demonstrate the
effectiveness and utility of the proposed CLEAR-DR system of enhancing the
interpretability of diagnostic grading results for the application of diabetic
retinopathy grading. Significance: CLEAR-DR can act as a potential powerful
tool to address the uninterpretability issue of current CAD systems, thus
improving their utility to clinicians.
A Study on Topological Descriptors for the Analysis of 3D Surface Texture
Authors: Matthias Zeppelzauer, Bartosz Zielinski, Mateusz Juda, Markus Seidl
Date: 2017-10-29 00:00:00
Tasks: General Classification

Methods from computational topology are becoming more and more popular in
computer vision and have shown to improve the state-of-the-art in several
tasks. In this paper, we investigate the applicability of topological
descriptors in the context of 3D surface analysis for the classification of
different surface textures. We present a comprehensive study on topological
descriptors, investigate their robustness and expressiveness and compare them
with state-of-the-art methods including Convolutional Neural Networks (CNNs).
Results show that class-specific information is reflected well in topological
descriptors. The investigated descriptors can directly compete with
non-topological descriptors and capture complementary information. As a
consequence they improve the state-of-the-art when combined with
non-topological descriptors.
If it ain't broke, don't fix it: Sparse metric repair
Authors: Anna C. Gilbert, Lalit Jain
Date: 2017-10-29 00:00:00

Many modern data-intensive computational problems either require, or benefit
from distance or similarity data that adhere to a metric. The algorithms run
faster or have better performance guarantees. Unfortunately, in real
applications, the data are messy and values are noisy. The distances between
the data points are far from satisfying a metric. Indeed, there are a number of
different algorithms for finding the closest set of distances to the given ones
that also satisfy a metric (sometimes with the extra condition of being
Euclidean). These algorithms can have unintended consequences, they can change
a large number of the original data points, and alter many other features of
the data.
  The goal of sparse metric repair is to make as few changes as possible to the
original data set or underlying distances so as to ensure the resulting
distances satisfy the properties of a metric. In other words, we seek to
minimize the sparsity (or the $\ell_0$ "norm") of the changes we make to the
distances subject to the new distances satisfying a metric. We give three
different combinatorial algorithms to repair a metric sparsely. In one setting
the algorithm is guaranteed to return the sparsest solution and in the other
settings, the algorithms repair the metric. Without prior information, the
algorithms run in time proportional to the cube of the number of input data
points and, with prior information we can reduce the running time considerably.
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute
Authors: Birgitta Dresp-Langley, John Mwangi Wandeto
Date: 2017-10-29 00:00:00
Tasks: Quantization, Time Series, Time Series Analysis

The quantization error (QE) from SOM applied on time series of spatial
contrast images with variable relative amount of white and dark pixel contents,
as in monochromatic medical images or satellite images, is proven a reliable
indicator of potentially critical changes in image homogeneity. The QE is shown
to increase linearly with the variability in spatial contrast contents across
time when contrast intensity is kept constant.
Deep Learning and Hierarchal Generative Models
Authors: Elchanan Mossel
Date: 2016-12-29 00:00:00
Tasks: All, Deep Learning

It is argued that deep learning is efficient for data that is generated from
hierarchal generative models. Examples of such generative models include
wavelet scattering networks, functions of compositional structure, and deep
rendering models. Unfortunately so far, for all such models, it is either not
rigorously known that they can be learned efficiently, or it is not known that
"deep algorithms" are required in order to learn them.
  We propose a simple family of "generative hierarchal models" which can be
efficiently learned and where "deep" algorithm are necessary for learning. Our
definition of "deep" algorithms is based on the empirical observation that deep
nets necessarily use correlations between features. More formally, we show that
in a semi-supervised setting, given access to low-order moments of the labeled
data and all of the unlabeled data, it is information theoretically impossible
to perform classification while at the same time there is an efficient
algorithm, that given all labelled and unlabeled data, perfectly labels all
unlabelled data with high probability.
  For the proof, we use and strengthen the fact that Belief Propagation does
not admit a good approximation in terms of linear functions.
Semi-Supervised Hierarchical Semantic Object Parsing
Authors: Jalal Mirakhorli, Hamidreza Amindavar
Date: 2017-09-23 00:00:00
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

Models based on Convolutional Neural Networks (CNNs) have been proven very
successful for semantic segmentation and object parsing that yield hierarchies
of features. Our key insight is to build convolutional networks that take input
of arbitrary size and produce object parsing output with efficient inference
and learning. In this work, we focus on the task of instance segmentation and
parsing which recognizes and localizes objects down to a pixel level base on
deep CNN. Therefore, unlike some related work, a pixel cannot belong to
multiple instances and parsing. Our model is based on a deep neural network
trained for object masking that supervised with input image and follow
incorporates a Conditional Random Field (CRF) with end-to-end trainable
piecewise order potentials based on object parsing outputs. In each CRF unit we
designed terms to capture the short range and long range dependencies from
various neighbors. The accurate instance-level segmentation that our network
produce is reflected by the considerable improvements obtained over previous
work at high APr thresholds. We demonstrate the effectiveness of our model with
extensive experiments on challenging dataset subset of PASCAL VOC2012.
Probabilistic Pursuits on Graphs
Authors: Michael Amir, Alfred M. Bruckstein
Date: 2017-10-23 00:00:00

We consider discrete dynamical systems of "ant-like" agents engaged in a
sequence of pursuits on a graph environment. The agents emerge one by one at
equal time intervals from a source vertex $s$ and pursue each other by greedily
attempting to close the distance to their immediate predecessor, the agent that
emerged just before them from $s$, until they arrive at the destination point
$t$. Such pursuits have been investigated before in the continuous setting and
in discrete time when the underlying environment is a regular grid. In both
these settings the agents' walks provably converge to a shortest path from $s$
to $t$. Furthermore, assuming a certain natural probability distribution over
the move choices of the agents on the grid (in case there are multiple shortest
paths between an agent and its predecessor), the walks converge to the uniform
distribution over all shortest paths from $s$ to $t$.
  We study the evolution of agent walks over a general finite graph environment
$G$. Our model is a natural generalization of the pursuit rule proposed for the
case of the grid. The main results are as follows. We show that "convergence"
to the shortest paths in the sense of previous work extends to all
pseudo-modular graphs (i.e. graphs in which every three pairwise intersecting
disks have a nonempty intersection), and also to environments obtained by
taking graph products, generalizing previous results in two different ways. We
show that convergence to the shortest paths is also obtained by chordal graphs,
and discuss some further positive and negative results for planar graphs. In
the most general case, convergence to the shortest paths is not guaranteed, and
the agents may get stuck on sets of recurrent, non-optimal walks from $s$ to
$t$. However, we show that the limiting distributions of the agents' walks will
always be uniform distributions over some set of walks of equal length.
Finding Dominant User Utterances And System Responses in Conversations
Authors: Dhiraj Madan, Sachindra Joshi
Date: 2017-10-29 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Clustering

There are several dialog frameworks which allow manual specification of
intents and rule based dialog flow. The rule based framework provides good
control to dialog designers at the expense of being more time consuming and
laborious. The job of a dialog designer can be reduced if we could identify
pairs of user intents and corresponding responses automatically from prior
conversations between users and agents. In this paper we propose an approach to
find these frequent user utterances (which serve as examples for intents) and
corresponding agent responses. We propose a novel SimCluster algorithm that
extends standard K-means algorithm to simultaneously cluster user utterances
and agent utterances by taking their adjacency information into account. The
method also aligns these clusters to provide pairs of intents and response
groups. We compare our results with those produced by using simple Kmeans
clustering on a real dataset and observe upto 10% absolute improvement in
F1-scores. Through our experiments on synthetic dataset, we show that our
algorithm gains more advantage over K-means algorithm when the data has large
variance.
Stochastic Gradient Descent in Continuous Time
Authors: Justin Sirignano, Konstantinos Spiliopoulos
Date: 2016-11-17 00:00:00

Stochastic gradient descent in continuous time (SGDCT) provides a
computationally efficient method for the statistical learning of
continuous-time models, which are widely used in science, engineering, and
finance. The SGDCT algorithm follows a (noisy) descent direction along a
continuous stream of data. SGDCT performs an online parameter update in
continuous time, with the parameter updates $\theta_t$ satisfying a stochastic
differential equation. We prove that $\lim_{t \rightarrow \infty} \nabla \bar
g(\theta_t) = 0$ where $\bar g$ is a natural objective function for the
estimation of the continuous-time dynamics. The convergence proof leverages
ergodicity by using an appropriate Poisson equation to help describe the
evolution of the parameters for large times. SGDCT can also be used to solve
continuous-time optimization problems, such as American options. For certain
continuous-time problems, SGDCT has some promising advantages compared to a
traditional stochastic gradient descent algorithm. As an example application,
SGDCT is combined with a deep neural network to price high-dimensional American
options (up to 100 dimensions).
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Authors: Yu Liu, Hongyang Li, Xiaogang Wang
Date: 2017-10-02 00:00:00
Tasks: Clustering, Metric Learning

Feature matters. How to train a deep network to acquire discriminative
features across categories and polymerized features within classes has always
been at the core of many computer vision tasks, specially for large-scale
recognition systems where test identities are unseen during training and the
number of classes could be at million scale. In this paper, we address this
problem based on the simple intuition that the cosine distance of features in
high-dimensional space should be close enough within one class and far away
across categories. To this end, we proposed the congenerous cosine (COCO)
algorithm to simultaneously optimize the cosine similarity among data. It
inherits the softmax property to make inter-class features discriminative as
well as shares the idea of class centroid in metric learning. Unlike previous
work where the center is a temporal, statistical variable within one mini-batch
during training, the formulated centroid is responsible for clustering
inner-class features to enforce them polymerized around the network truncus.
COCO is bundled with discriminative training and learned end-to-end with stable
convergence. Experiments on five benchmarks have been extensively conducted to
verify the effectiveness of our approach on both small-scale classification
task and large-scale human recognition problem.
A Convergent Algorithm for Bi-orthogonal Nonnegative Matrix Tri-Factorization
Authors: Andri Mirzal
Date: 2017-10-29 00:00:00
Tasks: Clustering

A convergent algorithm for nonnegative matrix factorization with
orthogonality constraints imposed on both factors is proposed in this paper.
This factorization concept was first introduced by Ding et al. with intent to
further improve clustering capability of NMF. However, as the original
algorithm was developed based on multiplicative update rules, the convergence
of the algorithm cannot be guaranteed. In this paper, we utilize the technique
presented in our previous work to develop the algorithm and prove that it
converges to a stationary point inside the solution space.
Regularization approaches for support vector machines with applications to biomedical data
Authors: Daniel Lopez-Martinez
Date: 2017-10-29 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Learning Theory

The support vector machine (SVM) is a widely used machine learning tool for
classification based on statistical learning theory. Given a set of training
data, the SVM finds a hyperplane that separates two different classes of data
points by the largest distance. While the standard form of SVM uses L2-norm
regularization, other regularization approaches are particularly attractive for
biomedical datasets where, for example, sparsity and interpretability of the
classifier's coefficient values are highly desired features. Therefore, in this
paper we consider different types of regularization approaches for SVMs, and
explore them in both synthetic and real biomedical datasets.
Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps
Authors: Edith Cohen, Shiri Chechik, Haim Kaplan
Date: 2017-06-12 00:00:00
Tasks: Clustering

Clustering of data points is a fundamental tool in data analysis. We consider
points $X$ in a relaxed metric space, where the triangle inequality holds
within a constant factor. The {\em cost} of clustering $X$ by $Q$ is
$V(Q)=\sum_{x\in X} d_{xQ}$. Two basic tasks, parametrized by $k \geq 1$, are
{\em cost estimation}, which returns (approximate) $V(Q)$ for queries $Q$ such
that $|Q|=k$ and {\em clustering}, which returns an (approximate) minimizer of
$V(Q)$ of size $|Q|=k$. With very large data sets $X$, we seek efficient
constructions of small samples that act as surrogates to the full data for
performing these tasks. Existing constructions that provide quality guarantees
are either worst-case, and unable to benefit from structure of real data sets,
or make explicit strong assumptions on the structure. We show here how to avoid
both these pitfalls using adaptive designs.
  At the core of our design is the {\em one2all} construction of
multi-objective probability-proportional-to-size (pps) samples: Given a set $M$
of centroids and $\alpha \geq 1$, one2all efficiently assigns probabilities to
points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq
V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha
|M|\epsilon^{-2})$. For cost queries, we can obtain worst-case sample size
$O(k\epsilon^{-2})$ by applying one2all to a bicriteria approximation $M$, but
we adaptively balance $|M|$ and $\alpha$ to further reduce sample size. For
clustering, we design an adaptive wrapper that applies a base clustering
algorithm to a sample $S$. Our wrapper uses the smallest sample that provides
statistical guarantees that the quality of the clustering on the sample carries
over to the full data set. We demonstrate experimentally the huge gains of
using our adaptive instead of worst-case methods.
Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
Authors: Aleksei Tiulpin, Jérôme Thevenot, Esa Rahtu, Petri Lehenkari, Simo Saarakkala
Date: 2017-10-29 00:00:00
Tasks: Decision Making

Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA
diagnosis is currently conducted by assessing symptoms and evaluating plain
radiographs, but this process suffers from subjectivity. In this study, we
present a new transparent computer-aided diagnosis method based on the Deep
Siamese Convolutional Neural Network to automatically score knee OA severity
according to the Kellgren-Lawrence grading scale. We trained our method using
the data solely from the Multicenter Osteoarthritis Study and validated it on
randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative
dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average
multiclass accuracy of 66.71\% compared to the annotations given by a committee
of clinical experts. Here, we also report a radiological OA diagnosis area
under the ROC curve of 0.93. We also present attention maps -- given as a class
probability distribution -- highlighting the radiological features affecting
the network decision. This information makes the decision process transparent
for the practitioner, which builds better trust toward automatic methods. We
believe that our model is useful for clinical decision making and for OA
research; therefore, we openly release our training codes and the data set
created in this study.