pwc-archive/papers-with-abstracts huggingface dataset:
Label Distribution Learning Forests
Authors: Wei Shen, Kai Zhao, Yilu Guo, Alan Yuille
Date: 2017-02-20 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Age Estimation, Representation Learning

Label distribution learning (LDL) is a general learning framework, which
assigns to an instance a distribution over a set of labels rather than a single
label or multiple labels. Current LDL methods have either restricted
assumptions on the expression form of the label distribution or limitations in
representation learning, e.g., to learn deep features in an end-to-end manner.
This paper presents label distribution learning forests (LDLFs) - a novel label
distribution learning algorithm based on differentiable decision trees, which
have several advantages: 1) Decision trees have the potential to model any
general form of label distributions by a mixture of leaf node predictions. 2)
The learning of differentiable decision trees can be combined with
representation learning. We define a distribution-based loss function for a
forest, enabling all the trees to be learned jointly, and show that an update
function for leaf node predictions, which guarantees a strict decrease of the
loss function, can be derived by variational bounding. The effectiveness of the
proposed LDLFs is verified on several LDL tasks and a computer vision
application, showing significant improvements to the state-of-the-art LDL
methods.
Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science
Authors: Yawei Hui, Yaohua Liu
Date: 2017-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Boundary Detection, Clustering

Recent advancements in neutron and X-ray sources, instrumentation and data
collection modes have significantly increased the experimental data size (which
could easily contain 10$^{8}$ -- 10$^{10}$ data points), so that conventional
volumetric visualization approaches become inefficient for both still imaging
and interactive OpenGL rendition in a 3D setting. We introduce a new approach
based on the unsupervised machine learning algorithm, Density-Based Spatial
Clustering of Applications with Noise (DBSCAN), to efficiently analyze and
visualize large volumetric datasets. Here we present two examples of analyzing
and visualizing datasets from the diffuse scattering experiment of a single
crystal sample and the tomographic reconstruction of a neutron scanning of a
turbine blade. We found that by using the intensity as the weighting factor in
the clustering process, DBSCAN becomes very effective in de-noising and
feature/boundary detection, and thus enables better visualization of the
hierarchical internal structures of the neutron scattering data.
Sparse Linear Isotonic Models
Authors: Sheng Chen, Arindam Banerjee
Date: 2017-10-16 00:00:00
Tasks: Additive models

In machine learning and data mining, linear models have been widely used to
model the response as parametric linear functions of the predictors. To relax
such stringent assumptions made by parametric linear models, additive models
consider the response to be a summation of unknown transformations applied on
the predictors; in particular, additive isotonic models (AIMs) assume the
unknown transformations to be monotone. In this paper, we introduce sparse
linear isotonic models (SLIMs) for highdimensional problems by hybridizing
ideas in parametric sparse linear models and AIMs, which enjoy a few appealing
advantages over both. In the high-dimensional setting, a two-step algorithm is
proposed for estimating the sparse parameters as well as the monotone functions
over predictors. Under mild statistical assumptions, we show that the algorithm
can accurately estimate the parameters. Promising preliminary experiments are
presented to support the theoretical results.
Convolutional Neural Networks for Sentiment Classification on Business Reviews
Authors: Andreea Salinca
Date: 2017-10-16 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Word Embeddings

Recently Convolutional Neural Networks (CNNs) models have proven remarkable
results for text classification and sentiment analysis. In this paper, we
present our approach on the task of classifying business reviews using word
embeddings on a large-scale dataset provided by Yelp: Yelp 2017 challenge
dataset. We compare word-based CNN using several pre-trained word embeddings
and end-to-end vector representations for text reviews classification. We
conduct several experiments to capture the semantic relationship between
business reviews and we use deep learning techniques that prove that the
obtained results are competitive with traditional methods.
Mapping higher-order network flows in memory and multilayer networks with Infomap
Authors: Daniel Edler, Ludvig Bohlin, Martin Rosvall
Date: 2017-06-15 00:00:00
Tasks: Community Detection

Comprehending complex systems by simplifying and highlighting important
dynamical patterns requires modeling and mapping higher-order network flows.
However, complex systems come in many forms and demand a range of
representations, including memory and multilayer networks, which in turn call
for versatile community-detection algorithms to reveal important modular
regularities in the flows. Here we show that various forms of higher-order
network flows can be represented in a unified way with networks that
distinguish physical nodes for representing a~complex system's objects from
state nodes for describing flows between the objects. Moreover, these so-called
sparse memory networks allow the information-theoretic community detection
method known as the map equation to identify overlapping and nested flow
modules in data from a range of~different higher-order interactions such as
multistep, multi-source, and temporal data. We derive the map equation applied
to sparse memory networks and describe its search algorithm Infomap, which can
exploit the flexibility of sparse memory networks. Together they provide a
general solution to reveal overlapping modular patterns in higher-order flows
through complex systems.
Gradient-free Policy Architecture Search and Adaptation
Authors: Sayna Ebrahimi, Anna Rohrbach, Trevor Darrell
Date: 2017-10-16 00:00:00
Tasks: Autonomous Driving, Neural Architecture Search

We develop a method for policy architecture search and adaptation via
gradient-free optimization which can learn to perform autonomous driving tasks.
By learning from both demonstration and environmental reward we develop a model
that can learn with relatively few early catastrophic failures. We first learn
an architecture of appropriate complexity to perceive aspects of world state
relevant to the expert demonstration, and then mitigate the effect of
domain-shift during deployment by adapting a policy demonstrated in a source
domain to rewards obtained in a target environment. We show that our approach
allows safer learning than baseline methods, offering a reduced cumulative
crash metric over the agent's lifetime as it learns to drive in a realistic
simulated environment.
Spectral Algorithms for Computing Fair Support Vector Machines
Authors: Matt Olfat, Anil Aswani
Date: 2017-10-16 00:00:00
Tasks: Fairness

Classifiers and rating scores are prone to implicitly codifying biases, which
may be present in the training data, against protected classes (i.e., age,
gender, or race). So it is important to understand how to design classifiers
and scores that prevent discrimination in predictions. This paper develops
computationally tractable algorithms for designing accurate but fair support
vector machines (SVM's). Our approach imposes a constraint on the covariance
matrices conditioned on each protected class, which leads to a nonconvex
quadratic constraint in the SVM formulation. We develop iterative algorithms to
compute fair linear and kernel SVM's, which solve a sequence of relaxations
constructed using a spectral decomposition of the nonconvex constraint. Its
effectiveness in achieving high prediction accuracy while ensuring fairness is
shown through numerical experiments on several data sets.
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Authors: Tomoya Sakai, Gang Niu, Masashi Sugiyama
Date: 2017-05-04 00:00:00
Tasks: imbalanced classification

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data (PU-AUC) and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Authors: Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen
Date: 2017-04-04 00:00:00
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization (BO) is a global optimization strategy designed to find
the minimum of an expensive black-box function, typically defined on a compact
subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate
model for the objective. Although currently available acquisition functions
address this goal with different degree of success, an over-exploration effect
of the contour of the search space is typically observed. However, in problems
like the configuration of machine learning algorithms, the function domain is
conservatively large and with a high probability the global minimum does not
sit on the boundary of the domain. We propose a method to incorporate this
knowledge into the search process by adding virtual derivative observations in
the \gp at the boundary of the search space. We use the properties of GPs to
impose conditions on the partial derivatives of the objective. The method is
applicable with any acquisition function, it is easy to use and consistently
reduces the number of evaluations required to optimize the objective
irrespective of the acquisition used. We illustrate the benefits of our
approach in an extensive experimental comparison.
On the Hardness of Inventory Management with Censored Demand Data
Authors: Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
Date: 2017-10-16 00:00:00
Tasks: Management

We consider a repeated newsvendor problem where the inventory manager has no
prior information about the demand, and can access only censored/sales data. In
analogy to multi-armed bandit problems, the manager needs to simultaneously
"explore" and "exploit" with her inventory decisions, in order to minimize the
cumulative cost. We make no probabilistic assumptions---importantly,
independence or time stationarity---regarding the mechanism that creates the
demand sequence. Our goal is to shed light on the hardness of the problem, and
to develop policies that perform well with respect to the regret criterion,
that is, the difference between the cumulative cost of a policy and that of the
best fixed action/static inventory decision in hindsight, uniformly over all
feasible demand sequences. We show that a simple randomized policy, termed the
Exponentially Weighted Forecaster, combined with a carefully designed cost
estimator, achieves optimal scaling of the expected regret (up to logarithmic
factors) with respect to all three key primitives: the number of time periods,
the number of inventory decisions available, and the demand support. Through
this result, we derive an important insight: the benefit from "information
stalking" as well as the cost of censoring are both negligible in this dynamic
learning problem, at least with respect to the regret criterion. Furthermore,
we modify the proposed policy in order to perform well in terms of the tracking
regret, that is, using as benchmark the best sequence of inventory decisions
that switches a limited number of times. Numerical experiments suggest that the
proposed approach outperforms existing ones (that are tailored to, or
facilitated by, time stationarity) on nonstationary demand models. Finally, we
extend the proposed approach and its analysis to a "combinatorial" version of
the repeated newsvendor problem.
Aligning Script Events with Narrative Texts
Authors: Simon Ostermann, Michael Roth, Stefan Thater, Manfred Pinkal
Date: 2017-10-16 00:00:00
Proceeding: SEMEVAL 2017 8

Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets. We introduce the task of mapping event mentions in narrative texts to such script event types, and present a model for this task that exploits rich linguistic representations as well as information on temporal ordering. The results of our experiments demonstrate that this complex task is indeed feasible.
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer
Authors: Giuseppe Jurman, Valerio Maggio, Diego Fioravanti, Ylenia Giarratano, Isotta Landi, Margherita Francescatto, Claudio Agostinelli, Marco Chierici, Manlio De Domenico, Cesare Furlanello
Date: 2017-10-16 00:00:00
Tasks: Deep Learning, Semantic Similarity, Semantic Textual Similarity

Convolutional Neural Networks (CNNs) are a popular deep learning architecture
widely applied in different domains, in particular in classifying over images,
for which the concept of convolution with a filter comes naturally.
Unfortunately, the requirement of a distance (or, at least, of a neighbourhood
function) in the input feature space has so far prevented its direct use on
data types such as omics data. However, a number of omics data are metrizable,
i.e., they can be endowed with a metric structure, enabling to adopt a
convolutional based deep learning framework, e.g., for prediction. We propose a
generalized solution for CNNs on omics data, implemented through a dedicated
Keras layer. In particular, for metagenomics data, a metric can be derived from
the patristic distance on the phylogenetic tree. For transcriptomics data, we
combine Gene Ontology semantic similarity and gene co-expression to define a
distance; the function is defined through a multilayer network where 3 layers
are defined by the GO mutual semantic similarity while the fourth one by gene
co-expression. As a general tool, feature distance on omics data is enabled by
OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning
framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for
Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a
metagenomics collection of gut microbiota of 222 IBD patients.
NiftyNet: a deep-learning platform for medical imaging
Authors: Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I. Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C. Barratt, Sébastien Ourselin, M. Jorge Cardoso, Tom Vercauteren
Date: 2017-09-11 00:00:00
Tasks: Data Augmentation, Deep Learning, Image Generation, Medical Image Analysis, Medical Image Generation, regression, Representation Learning, Segmentation

Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
  NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
  We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
  NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.
Offline Handwritten Signature Verification - Literature Review
Authors: Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Date: 2015-07-28 00:00:00

The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.
What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling
Authors: İlker Bozcan, Yağmur Oymak, İdil Zeynep Alemdar, Sinan Kalkan
Date: 2017-10-16 00:00:00
Tasks: General Classification, Scene Classification

Scene models allow robots to reason about what is in the scene, what else
should be in it, and what should not be in it. In this paper, we propose a
hybrid Boltzmann Machine (BM) for scene modeling where relations between
objects are integrated. To be able to do that, we extend BM to include tri-way
edges between visible (object) nodes and make the network to share the
relations across different objects. We evaluate our method against several
baseline models (Deep Boltzmann Machines, and Restricted Boltzmann Machines) on
a scene classification dataset, and show that it performs better in several
scene reasoning tasks.
Large Scale Graph Learning from Smooth Signals
Authors: Vassilis Kalofolias, Nathanaël Perraudin
Date: 2017-10-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Graph Learning

Graphs are a prevalent tool in data science, as they model the inherent
structure of the data. They have been used successfully in unsupervised and
semi-supervised learning. Typically they are constructed either by connecting
nearest samples, or by learning them from data, solving an optimization
problem. While graph learning does achieve a better quality, it also comes with
a higher computational cost. In particular, the current state-of-the-art model
cost is $\mathcal{O}(n^2)$ for $n$ samples. In this paper, we show how to scale
it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$,
with quality that approaches the exact graph learning model. Our algorithm uses
known approximate nearest neighbor techniques to reduce the number of
variables, and automatically selects the correct parameters of the model,
requiring a single intuitive input: the desired edge density.
Causal Inference on Multivariate and Mixed-Type Data
Authors: Alexander Marx, Jilles Vreeken
Date: 2017-02-21 00:00:00
Tasks: Causal Inference, Vocal Bursts Type Prediction

Given data over the joint distribution of two random variables $X$ and $Y$,
we consider the problem of inferring the most likely causal direction between
$X$ and $Y$. In particular, we consider the general case where both $X$ and $Y$
may be univariate or multivariate, and of the same or mixed data types. We take
an information theoretic approach, based on Kolmogorov complexity, from which
it follows that first describing the data over cause and then that of effect
given cause is shorter than the reverse direction.
  The ideal score is not computable, but can be approximated through the
Minimum Description Length (MDL) principle. Based on MDL, we propose two
scores, one for when both $X$ and $Y$ are of the same single data type, and one
for when they are mixed-type. We model dependencies between $X$ and $Y$ using
classification and regression trees. As inferring the optimal model is NP-hard,
we propose Crack, a fast greedy algorithm to determine the most likely causal
direction directly from the data.
  Empirical evaluation on a wide range of data shows that Crack reliably, and
with high accuracy, infers the correct causal direction on both univariate and
multivariate cause-effect pairs over both single and mixed-type data.
Convolutional Low-Resolution Fine-Grained Classification
Authors: Dingding Cai, Ke Chen, Yanlin Qian, Joni-Kristian Kämäräinen
Date: 2017-03-15 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Image Super-Resolution, Super-Resolution

Successful fine-grained image classification methods learn subtle details
between visually similar (sub-)classes, but the problem becomes significantly
more challenging if the details are missing due to low resolution. Encouraged
by the recent success of Convolutional Neural Network (CNN) architectures in
image classification, we propose a novel resolution-aware deep model which
combines convolutional image super-resolution and convolutional fine-grained
classification into a single model in an end-to-end manner. Extensive
experiments on the Stanford Cars and Caltech-UCSD Birds 200-2011 benchmarks
demonstrate that the proposed model consistently performs better than
conventional convolutional net on classifying fine-grained object classes in
low-resolution images.