pwc-archive/papers-with-abstracts huggingface dataset:
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Authors: Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon
Date: 2016-12-11 00:00:00
Tasks: Benchmarking, Document Classification, Multiple Instance Learning, Survey, Weakly-supervised Learning

Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research.
Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Authors: Feng Chen, Baojian Zhou
Date: 2016-12-11 00:00:00

Sparsity-constrained optimization is an important and challenging problem
that has wide applicability in data mining, machine learning, and statistics.
In this paper, we focus on sparsity-constrained optimization in cases where the
cost function is a general nonlinear function and, in particular, the sparsity
constraint is defined by a graph-structured sparsity model. Existing methods
explore this problem in the context of sparse estimation in linear models. To
the best of our knowledge, this is the first work to present an efficient
approximation algorithm, namely, Graph-structured Matching Pursuit (Graph-Mp),
to optimize a general nonlinear function subject to graph-structured
constraints. We prove that our algorithm enjoys the strong guarantees analogous
to those designed for linear models in terms of convergence rate and
approximation accuracy. As a case study, we specialize Graph-Mp to optimize a
number of well-known graph scan statistic models for the connected subgraph
detection task, and empirical evidence demonstrates that our general algorithm
performs superior over state-of-the-art methods that are designed specifically
for the task of connected subgraph detection.
Non-negative Factorization of the Occurrence Tensor from Financial Contracts
Authors: Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein
Date: 2016-12-10 00:00:00

We propose an algorithm for the non-negative factorization of an occurrence
tensor built from heterogeneous networks. We use l0 norm to model sparse errors
over discrete values (occurrences), and use decomposed factors to model the
embedded groups of nodes. An efficient splitting method is developed to
optimize the nonconvex and nonsmooth objective. We study both synthetic
problems and a new dataset built from financial documents, resMBS.
An Empirical Study of ADMM for Nonconvex Problems
Authors: Zheng Xu, Soham De, Mario Figueiredo, Christoph Studer, Tom Goldstein
Date: 2016-12-10 00:00:00
Tasks: Denoising, Image Denoising, regression, Retrieval

The alternating direction method of multipliers (ADMM) is a common
optimization tool for solving constrained and non-differentiable problems. We
provide an empirical study of the practical performance of ADMM on several
nonconvex applications, including l0 regularized linear regression, l0
regularized image denoising, phase retrieval, and eigenvector computation. Our
experiments suggest that ADMM performs well on a broad class of non-convex
problems. Moreover, recently proposed adaptive ADMM methods, which
automatically tune penalty parameters as the method runs, can improve algorithm
efficiency and solution quality compared to ADMM with a non-tuned penalty.
Semi-Supervised Learning for Neural Machine Translation
Authors: Yong Cheng, Wei Xu, Zhongjun He, wei he, Hua Wu, Maosong Sun, Yang Liu
Date: 2016-06-15 00:00:00
Proceeding: ACL 2016 8
Tasks: Decoder, Machine Translation, NMT, parameter estimation, Translation

While end-to-end neural machine translation (NMT) has made remarkable
progress recently, NMT systems only rely on parallel corpora for parameter
estimation. Since parallel corpora are usually limited in quantity, quality,
and coverage, especially for low-resource languages, it is appealing to exploit
monolingual corpora to improve NMT. We propose a semi-supervised approach for
training NMT models on the concatenation of labeled (parallel corpora) and
unlabeled (monolingual corpora) data. The central idea is to reconstruct the
monolingual corpora using an autoencoder, in which the source-to-target and
target-to-source translation models serve as the encoder and decoder,
respectively. Our approach can not only exploit the monolingual corpora of the
target language, but also of the source language. Experiments on the
Chinese-English dataset show that our approach achieves significant
improvements over state-of-the-art SMT and NMT systems.
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
Authors: Yanbo Xu, Yanxun Xu, Suchi Saria
Date: 2016-08-18 00:00:00
Tasks: Decision Making, Kidney Function, Time Series, Time Series Analysis

We study the problem of estimating the continuous response over time to
interventions using observational time series---a retrospective dataset where
the policy by which the data are generated is unknown to the learner. We are
motivated by applications where response varies by individuals and therefore,
estimating responses at the individual-level is valuable for personalizing
decision-making. We refer to this as the problem of estimating individualized
treatment response (ITR) curves. In statistics, G-computation formula (Robins,
1986) has been commonly used for estimating treatment responses from
observational data containing sequential treatment assignments. However, past
studies have focused predominantly on obtaining point-in-time estimates at the
population level. We leverage the G-computation formula and develop a novel
Bayesian nonparametric (BNP) method that can flexibly model functional data and
provide posterior inference over the treatment response curves at both the
individual and population level. On a challenging dataset containing time
series from patients admitted to a hospital, we estimate responses to
treatments used in managing kidney function and show that the resulting fits
are more accurate than alternative approaches. Accurate methods for obtaining
ITRs from observational data can dramatically accelerate the pace at which
personalized treatment plans become possible.
What makes ImageNet good for transfer learning?
Authors: Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
Date: 2016-08-30 00:00:00
Tasks: Action Classification, General Classification, Scene Classification, Transfer Learning

The tremendous success of ImageNet-trained deep features on a wide range of
transfer tasks begs the question: what are the properties of the ImageNet
dataset that are critical for learning good, general-purpose features? This
work provides an empirical investigation of various facets of this question: Is
more pre-training data always better? How does feature quality depend on the
number of training examples per class? Does adding more object classes improve
performance? For the same data budget, how should the data be split into
classes? Is fine-grained recognition necessary for learning good features?
Given the same number of training classes, is it better to have coarse classes
or fine-grained classes? Which is better: more classes or more examples per
class? To answer these and related questions, we pre-trained CNN features on
various subsets of the ImageNet dataset and evaluated transfer performance on
PASCAL detection, PASCAL action classification, and SUN scene classification
tasks. Our overall findings suggest that most changes in the choice of
pre-training data long thought to be critical do not significantly affect
transfer performance.? Given the same number of training classes, is it better
to have coarse classes or fine-grained classes? Which is better: more classes
or more examples per class?
Salient Object Detection with Convex Hull Overlap
Authors: Yongqing Liang
Date: 2016-12-10 00:00:00
Tasks: Object, object-detection, Object Detection, Salient Object Detection

Salient object detection plays an important part in a vision system to detect important regions. Convolutional neural network (CNN) based methods directly train their models with large-scale datasets, but what is the crucial feature for saliency is still a problem. In this paper, we establish a novel bottom-up feature named convex hull overlap (CHO), combining with appearance contrast features, to detect salient objects. CHO feature is a kind of enhanced Gestalt cue. Psychologists believe that surroundedness reflects objects overlap relationship. An object which is on the top of the others is attractive. Our method significantly differs from other earlier works in (1) We set up a hand-crafted feature to detect salient object that our model does not need to be trained by large-scale datasets; (2) Previous works only focus on appearance features, while our CHO feature makes up the gap between the spatial object covering and the object's saliency. Our experiments on a large number of public datasets have obtained very positive results.
Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation
Authors: Samir Bhatt, Ewan Cameron, Seth R. Flaxman, Daniel J Weiss, David L Smith, Peter W Gething
Date: 2016-12-10 00:00:00
Tasks: Spatial Interpolation

Maps of infectious disease---charting spatial variations in the force of
infection, degree of endemicity, and the burden on human health---provide an
essential evidence base to support planning towards global health targets.
Contemporary disease mapping efforts have embraced statistical modelling
approaches to properly acknowledge uncertainties in both the available
measurements and their spatial interpolation. The most common such approach is
that of Gaussian process regression, a mathematical framework comprised of two
components: a mean function harnessing the predictive power of multiple
independent variables, and a covariance function yielding spatio-temporal
shrinkage against residual variation from the mean. Though many techniques have
been developed to improve the flexibility and fitting of the covariance
function, models for the mean function have typically been restricted to simple
linear terms. For infectious diseases, known to be driven by complex
interactions between environmental and socio-economic factors, improved
modelling of the mean function can greatly boost predictive power. Here we
present an ensemble approach based on stacked generalisation that allows for
multiple, non-linear algorithmic mean functions to be jointly embedded within
the Gaussian process framework. We apply this method to mapping Plasmodium
falciparum prevalence data in Sub-Saharan Africa and show that the generalised
ensemble approach markedly out-performs any individual method.
Data Curation APIs
Authors: Seyed-Mehdi-Reza Beheshti, Alireza Tabebordbar, Boualem Benatallah, Reza Nouri
Date: 2016-12-10 00:00:00

Understanding and analyzing big data is firmly recognized as a powerful and
strategic priority. For deeper interpretation of and better intelligence with
big data, it is important to transform raw data (unstructured, semi-structured
and structured data sources, e.g., text, video, image data sets) into curated
data: contextualized data and knowledge that is maintained and made available
for use by end-users and applications. In particular, data curation acts as the
glue between raw data and analytics, providing an abstraction layer that
relieves users from time consuming, tedious and error prone curation tasks. In
this context, the data curation process becomes a vital analytics asset for
increasing added value and insights.
  In this paper, we identify and implement a set of curation APIs and make them
available (on GitHub) to researchers and developers to assist them transforming
their raw data into curated data. The curation APIs enable developers to easily
add features - such as extracting keyword, part of speech, and named entities
such as Persons, Locations, Organizations, Companies, Products, Diseases,
Drugs, etc.; providing synonyms and stems for extracted information items
leveraging lexical knowledge bases for the English language such as WordNet;
linking extracted entities to external knowledge bases such as Google Knowledge
Graph and Wikidata; discovering similarity among the extracted information
items, such as calculating similarity between string, number, date and time
data; classifying, sorting and categorizing data into various types, forms or
any other distinct class; and indexing structured and unstructured data - into
their applications.
Intrinsic normalization and extrinsic denormalization of formant data of vowels
Authors: T. V. Ananthapadmanabha, A. G. Ramakrishnan
Date: 2016-09-16 00:00:00
Tasks: General Classification, Vowel Classification

Using a known speaker-intrinsic normalization procedure, formant data are
scaled by the reciprocal of the geometric mean of the first three formant
frequencies. This reduces the influence of the talker but results in a
distorted vowel space. The proposed speaker-extrinsic procedure re-scales the
normalized values by the mean formant values of vowels. When tested on the
formant data of vowels published by Peterson and Barney, the combined approach
leads to well separated clusters by reducing the spread due to talkers. The
proposed procedure performs better than two top-ranked normalization procedures
based on the accuracy of vowel classification as the objective measure.
Towards an Automated Image De-fencing Algorithm Using Sparsity
Authors: Sankaraganesh Jonna, Krishna K. Nakka, Rajiv R. Sahay
Date: 2016-12-10 00:00:00
Tasks: Motion Estimation, Position

Conventional approaches to image de-fencing suffer from non-robust fence
detection and are limited to processing images of static scenes. In this
position paper, we propose an automatic de-fencing algorithm for images of
dynamic scenes. We divide the problem of image de-fencing into the tasks of
automated fence detection, motion estimation and fusion of data from multiple
frames of a captured video of the dynamic scene. Fences are detected
automatically using two approaches, namely, employing Gabor filter and a
machine learning method. We cast the fence removal problem in an optimization
framework, by modeling the formation of the degraded observations. The inverse
problem is solved using split Bregman technique assuming total variation of the
de-fenced image as the regularization constraint.
Generalized Deep Image to Image Regression
Authors: Venkataraman Santhanam, Vlad I. Morariu, Larry S. Davis
Date: 2016-12-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Colorization, Denoising, Image-to-Image Regression, regression

We present a Deep Convolutional Neural Network architecture which serves as a
generic image-to-image regressor that can be trained end-to-end without any
further machinery. Our proposed architecture: the Recursively Branched
Deconvolutional Network (RBDN) develops a cheap multi-context image
representation very early on using an efficient recursive branching scheme with
extensive parameter sharing and learnable upsampling. This multi-context
representation is subjected to a highly non-linear locality preserving
transformation by the remainder of our network comprising of a series of
convolutions/deconvolutions without any spatial downsampling. The RBDN
architecture is fully convolutional and can handle variable sized images during
inference. We provide qualitative/quantitative results on $3$ diverse tasks:
relighting, denoising and colorization and show that our proposed RBDN
architecture obtains comparable results to the state-of-the-art on each of
these tasks when used off-the-shelf without any post processing or
task-specific architectural modifications.
A Character-Word Compositional Neural Language Model for Finnish
Authors: Matti Lankinen, Hannes Heikinheimo, Pyry Takala, Tapani Raiko, Juha Karhunen
Date: 2016-12-10 00:00:00
Tasks: Language Modeling, Language Modelling

Inspired by recent research, we explore ways to model the highly
morphological Finnish language at the level of characters while maintaining the
performance of word-level models. We propose a new
Character-to-Word-to-Character (C2W2C) compositional language model that uses
characters as input and output while still internally processing word level
embeddings. Our preliminary experiments, using the Finnish Europarl V7 corpus,
indicate that C2W2C can respond well to the challenges of morphologically rich
languages such as high out of vocabulary rates, the prediction of novel words,
and growing vocabulary size. Notably, the model is able to correctly score
inflectional forms that are not present in the training data and sample
grammatically and semantically correct Finnish sentences character by
character.
Real-time eSports Match Result Prediction
Authors: Yifan Yang, Tian Qin, Yu-Heng Lei
Date: 2016-12-10 00:00:00
Tasks: Attribute, Dota 2, Prediction, regression

In this paper, we try to predict the winning team of a match in the
multiplayer eSports game Dota 2. To address the weaknesses of previous work, we
consider more aspects of prior (pre-match) features from individual players'
match history, as well as real-time (during-match) features at each minute as
the match progresses. We use logistic regression, the proposed Attribute
Sequence Model, and their combinations as the prediction models. In a dataset
of 78362 matches where 20631 matches contain replay data, our experiments show
that adding more aspects of prior features improves accuracy from 58.69% to
71.49%, and introducing real-time features achieves up to 93.73% accuracy when
predicting at the 40th minute.
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration
Authors: Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz
Date: 2016-10-28 00:00:00

Predictive models deployed in the real world may assign incorrect labels to
instances with high confidence. Such errors or unknown unknowns are rooted in
model incompleteness, and typically arise because of the mismatch between
training data and the cases encountered at test time. As the models are blind
to such errors, input from an oracle is needed to identify these failures. In
this paper, we formulate and address the problem of informed discovery of
unknown unknowns of any given predictive model where unknown unknowns occur due
to systematic biases in the training data. We propose a model-agnostic
methodology which uses feedback from an oracle to both identify unknown
unknowns and to intelligently guide the discovery. We employ a two-phase
approach which first organizes the data into multiple partitions based on the
feature similarity of instances and the confidence scores assigned by the
predictive model, and then utilizes an explore-exploit strategy for discovering
unknown unknowns across these partitions. We demonstrate the efficacy of our
framework by varying the underlying causes of unknown unknowns across various
applications. To the best of our knowledge, this paper presents the first
algorithmic approach to the problem of discovering unknown unknowns of
predictive models.
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
Authors: Shusen Wang, Zhihua Zhang, Tong Zhang
Date: 2015-03-29 00:00:00

Symmetric positive semi-definite (SPSD) matrix approximation methods have
been extensively used to speed up large-scale eigenvalue computation and kernel
learning methods. The standard sketch based method, which we call the prototype
model, produces relatively accurate approximations, but is inefficient on large
square matrices. The Nystr\"om method is highly efficient, but can only achieve
low accuracy. In this paper we propose a novel model that we call the {\it fast
SPSD matrix approximation model}. The fast model is nearly as efficient as the
Nystr\"om method and as accurate as the prototype model. We show that the fast
model can potentially solve eigenvalue problems and kernel learning problems in
linear time with respect to the matrix size $n$ to achieve $1+\epsilon$
relative-error, whereas both the prototype model and the Nystr\"om method cost
at least quadratic time to attain comparable error bound. Empirical comparisons
among the prototype model, the Nystr\"om method, and our fast model demonstrate
the superiority of the fast model. We also contribute new understandings of the
Nystr\"om method. The Nystr\"om method is a special instance of our fast model
and is approximation to the prototype model. Our technique can be
straightforwardly applied to make the CUR matrix decomposition more efficiently
computed without much affecting the accuracy.
A natural language interface to a graph-based bibliographic information retrieval system
Authors: Yongjun Zhu, Erjia Yan, Il-Yeol Song
Date: 2016-12-10 00:00:00
Tasks: Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Queries, Retrieval

With the ever-increasing scientific literature, there is a need on a natural
language interface to bibliographic information retrieval systems to retrieve
related information effectively. In this paper, we propose a natural language
interface, NLI-GIBIR, to a graph-based bibliographic information retrieval
system. In designing NLI-GIBIR, we developed a novel framework that can be
applicable to graph-based bibliographic information retrieval systems. Our
framework integrates algorithms/heuristics for interpreting and analyzing
natural language bibliographic queries. NLI-GIBIR allows users to search for a
variety of bibliographic data through natural language. A series of text- and
linguistic-based techniques are used to analyze and answer natural language
queries, including tokenization, named entity recognition, and syntactic
analysis. We find that our framework can effectively represents and addresses
complex bibliographic information needs. Thus, the contributions of this paper
are as follows: First, to our knowledge, it is the first attempt to propose a
natural language interface to graph-based bibliographic information retrieval.
Second, we propose a novel customized natural language processing framework
that integrates a few original algorithms/heuristics for interpreting and
analyzing natural language bibliographic queries. Third, we show that the
proposed framework and natural language interface provide a practical solution
in building real-world natural language interface-based bibliographic
information retrieval systems. Our experimental results show that the presented
system can correctly answer 39 out of 40 example natural language queries with
varying lengths and complexities.