pwc-archive/papers-with-abstracts huggingface dataset:
Hypotheses testing on infinite random graphs
Authors: Daniil Ryabko
Date: 2017-08-10 00:00:00
Tasks: Time Series, Time Series Analysis

Drawing on some recent results that provide the formalism necessary to
definite stationarity for infinite random graphs, this paper initiates the
study of statistical and learning questions pertaining to these objects.
Specifically, a criterion for the existence of a consistent test for complex
hypotheses is presented, generalizing the corresponding results on time series.
As an application, it is shown how one can test that a tree has the Markov
property, or, more generally, to estimate its memory.
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering
Authors: Zhou Yu, Jun Yu, Chenchao Xiang, Jianping Fan, DaCheng Tao
Date: 2017-08-10 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA), Vocal Bursts Intensity Prediction

Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multi-modal feature fusion that is able to capture the complex interactions between multi-modal features; 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and question representations, a `co-attention' mechanism is developed by using a deep neural network architecture to jointly learn the attentions for both the image and the question, which can allow us to reduce the irrelevant features effectively and obtain more discriminative features for image and question representations. For multi-modal feature fusion, a generalized Multi-modal Factorized High-order pooling approach (MFH) is developed to achieve more effective fusion of multi-modal features by exploiting their correlations sufficiently, which can further result in superior VQA performance as compared with the state-of-the-art approaches. For answer prediction, the KL (Kullback-Leibler) divergence is used as the loss function to achieve precise characterization of the complex correlations between multiple diverse answers with the same or similar meaning, which can allow us to achieve faster convergence rate and obtain slightly better accuracy on answer prediction. A deep neural network architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance. With an ensemble of our MFH models, we achieve the state-of-the-art performance on the large-scale VQA datasets and win the runner-up in VQA Challenge 2017.
Slack and Margin Rescaling as Convex Extensions of Supermodular Functions
Authors: Matthew B. Blaschko
Date: 2016-06-19 00:00:00
Tasks: Image Segmentation, Object Localization, Semantic Segmentation, Structured Prediction

Slack and margin rescaling are variants of the structured output SVM, which
is frequently applied to problems in computer vision such as image
segmentation, object localization, and learning parts based object models. They
define convex surrogates to task specific loss functions, which, when
specialized to non-additive loss functions for multi-label problems, yield
extensions to increasing set functions. We demonstrate in this paper that we
may use these concepts to define polynomial time convex extensions of arbitrary
supermodular functions, providing an analysis framework for the tightness of
these surrogates. This analysis framework shows that, while neither margin nor
slack rescaling dominate the other, known bounds on supermodular functions can
be used to derive extensions that dominate both of these, indicating possible
directions for defining novel structured output prediction surrogates. In
addition to the analysis of structured prediction loss functions, these results
imply an approach to supermodular minimization in which margin rescaling is
combined with non-polynomial time convex extensions to compute a sequence of LP
relaxations reminiscent of a cutting plane method. This approach is applied to
the problem of selecting representative exemplars from a set of images,
validating our theoretical contributions.
Modality-bridge Transfer Learning for Medical Image Classification
Authors: Hak Gu Kim, Yeoreum Choi, Yong Man Ro
Date: 2017-08-10 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Medical Image Classification, Transfer Learning

This paper presents a new approach of transfer learning-based medical image
classification to mitigate insufficient labeled data problem in medical domain.
Instead of direct transfer learning from source to small number of labeled
target data, we propose a modality-bridge transfer learning which employs the
bridge database in the same medical imaging acquisition modality as target
database. By learning the projection function from source to bridge and from
bridge to target, the domain difference between source (e.g., natural images)
and target (e.g., X-ray images) can be mitigated. Experimental results show
that the proposed method can achieve a high classification performance even for
a small number of labeled target medical images, compared to various transfer
learning approaches.
Deep Alignment Network: A convolutional neural network for robust face alignment
Authors: Marek Kowalski, Jacek Naruniec, Tomasz Trzcinski
Date: 2017-06-06 00:00:00
Tasks: Face Alignment, Keypoint Detection, Robust Face Alignment

In this paper, we propose Deep Alignment Network (DAN), a robust face
alignment method based on a deep neural network architecture. DAN consists of
multiple stages, where each stage improves the locations of the facial
landmarks estimated by the previous stage. Our method uses entire face images
at all stages, contrary to the recently proposed face alignment methods that
rely on local patches. This is possible thanks to the use of landmark heatmaps
which provide visual information about landmark locations estimated at the
previous stages of the algorithm. The use of entire face images rather than
patches allows DAN to handle face images with large variation in head pose and
difficult initializations. An extensive evaluation on two publicly available
datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%.
Our method has also been submitted for evaluation as part of the Menpo
challenge.
Semantic Video CNNs through Representation Warping
Authors: Raghudeep Gadde, Varun Jampani, Peter V. Gehler
Date: 2017-08-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Semantic Segmentation

In this work, we propose a technique to convert CNN models for semantic
segmentation of static images into CNNs for video data. We describe a warping
method that can be used to augment existing architectures with very little
extra computational cost. This module is called NetWarp and we demonstrate its
use for a range of network architectures. The main design principle is to use
optical flow of adjacent frames for warping internal network representations
across time. A key insight of this work is that fast optical flow methods can
be combined with many different CNN architectures for improved performance and
end-to-end training. Experiments validate that the proposed approach incurs
only little extra computational cost, while improving performance, when video
streams are available. We achieve new state-of-the-art results on the CamVid
and Cityscapes benchmark datasets and show consistent improvements over
different baseline networks. Our code and models will be available at
http://segmentation.is.tue.mpg.de
Geospatial Semantics
Authors: Yingjie Hu
Date: 2017-07-12 00:00:00
Tasks: Information Retrieval, Retrieval

Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
Authors: Zizhao Zhang, Pingjun Chen, Manish Sapkota, Lin Yang
Date: 2017-08-10 00:00:00
Tasks: Diagnostic, Language Modeling, Language Modelling, Prediction

In this paper, we introduce the semantic knowledge of medical images from
their diagnostic reports to provide an inspirational network training and an
interpretable prediction mechanism with our proposed novel multimodal neural
network, namely TandemNet. Inside TandemNet, a language model is used to
represent report text, which cooperates with the image model in a tandem
scheme. We propose a novel dual-attention model that facilitates high-level
interactions between visual and semantic information and effectively distills
useful features for prediction. In the testing stage, TandemNet can make
accurate image prediction with an optional report text input. It also
interprets its prediction by producing attention on the image and text
informative feature pieces, and further generating diagnostic report
paragraphs. Based on a pathological bladder cancer images and their diagnostic
reports (BCIDR) dataset, sufficient experiments demonstrate that our method
effectively learns and integrates knowledge from multimodalities and obtains
significantly improved performance than comparing baselines.
The Variational Fair Autoencoder
Authors: Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
Date: 2015-11-03 00:00:00
Tasks: General Classification, Sentiment Analysis

We investigate the problem of learning representations that are invariant to
certain nuisance or sensitive factors of variation in the data while retaining
as much of the remaining information as possible. Our model is based on a
variational autoencoding architecture with priors that encourage independence
between sensitive and latent factors of variation. Any subsequent processing,
such as classification, can then be performed on this purged latent
representation. To remove any remaining dependencies we incorporate an
additional penalty term based on the "Maximum Mean Discrepancy" (MMD) measure.
We discuss how these architectures can be efficiently trained on data and show
in experiments that this method is more effective than previous work in
removing unwanted sources of variation while maintaining informative latent
representations.
Communication-Free Parallel Supervised Topic Models
Authors: Lee Gao, Ronghuo Zheng
Date: 2017-08-10 00:00:00
Tasks: Topic Models

Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC)
methods are commonly used in learning graphical models. However, MCMC cannot be
directly applied in learning topic models because of the quasi-ergodicity
problem caused by multimodal distribution of topics. In this paper, we develop
an embarrassingly parallel MCMC algorithm for sLDA. Our algorithm works by
switching the order of sampled topics combination and labeling variable
prediction in sLDA, in which it overcomes the quasi-ergodicity problem because
high-dimension topics that follow a multimodal distribution are projected into
one-dimension document labels that follow a unimodal distribution. Our
empirical experiments confirm that the out-of-sample prediction performance
using our embarrassingly parallel algorithm is comparable to non-parallel sLDA
while the computation time is significantly reduced.
When Is the First Spurious Variable Selected by Sequential Regression Procedures?
Authors: Weijie J. Su
Date: 2017-08-10 00:00:00
Tasks: Model Selection, regression

Applied statisticians use sequential regression procedures to produce a
ranking of explanatory variables and, in settings of low correlations between
variables and strong true effect sizes, expect that variables at the very top
of this ranking are truly relevant to the response. In a regime of certain
sparsity levels, however, three examples of sequential procedures--forward
stepwise, the lasso, and least angle regression--are shown to include the first
spurious variable unexpectedly early. We derive a rigorous, sharp prediction of
the rank of the first spurious variable for these three procedures,
demonstrating that the first spurious variable occurs earlier and earlier as
the regression coefficients become denser. This counterintuitive phenomenon
persists for statistically independent Gaussian random designs and an
arbitrarily large magnitude of the true effects. We gain a better understanding
of the phenomenon by identifying the underlying cause and then leverage the
insights to introduce a simple visualization tool termed the double-ranking
diagram to improve on sequential methods. As a byproduct of these findings, we
obtain the first provable result certifying the exact equivalence between the
lasso and least angle regression in the early stages of solution paths beyond
orthogonal designs. This equivalence can seamlessly carry over many important
model selection results concerning the lasso to least angle regression.
Learning Graph While Training: An Evolving Graph Convolutional Neural Network
Authors: Ruoyu Li, Junzhou Huang
Date: 2017-08-10 00:00:00
Tasks: Clustering

Convolution Neural Networks on Graphs are important generalization and
extension of classical CNNs. While previous works generally assumed that the
graph structures of samples are regular with unified dimensions, in many
applications, they are highly diverse or even not well defined. Under some
circumstances, e.g. chemical molecular data, clustering or coarsening for
simplifying the graphs is hard to be justified chemically. In this paper, we
propose a more general and flexible graph convolution network (EGCN) fed by
batch of arbitrarily shaped data together with their evolving graph Laplacians
trained in supervised fashion. Extensive experiments have been conducted to
demonstrate the superior performance in terms of both the acceleration of
parameter fitting and the significantly improved prediction accuracy on
multiple graph-structured datasets.
"Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
Authors: Ting-Hao Kenneth Huang, Walter S. Lasecki, Amos Azaria, Jeffrey P. Bigham
Date: 2017-08-10 00:00:00
Tasks: Sentence

Intelligent conversational assistants, such as Apple's Siri, Microsoft's
Cortana, and Amazon's Echo, have quickly become a part of our digital life.
However, these assistants have major limitations, which prevents users from
conversing with them as they would with human dialog partners. This limits our
ability to observe how users really want to interact with the underlying
system. To address this problem, we developed a crowd-powered conversational
assistant, Chorus, and deployed it to see how users and workers would interact
together when mediated by the system. Chorus sophisticatedly converses with end
users over time by recruiting workers on demand, which in turn decide what
might be the best response for each user sentence. Up to the first month of our
deployment, 59 users have held conversations with Chorus during 320
conversational sessions. In this paper, we present an account of Chorus'
deployment, with a focus on four challenges: (i) identifying when conversations
are over, (ii) malicious users and workers, (iii) on-demand recruiting, and
(iv) settings in which consensus is not enough. Our observations could assist
the deployment of crowd-powered conversation systems and crowd-powered systems
in general.
A Unified Model for Near and Remote Sensing
Authors: Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: Density Estimation, model

We propose a novel convolutional neural network architecture for estimating
geospatial functions such as population density, land cover, or land use. In
our approach, we combine overhead and ground-level images in an end-to-end
trainable neural network, which uses kernel regression and density estimation
to convert features extracted from the ground-level images into a dense feature
map. The output of this network is a dense estimate of the geospatial function
in the form of a pixel-level labeling of the overhead image. To evaluate our
approach, we created a large dataset of overhead and ground-level images from a
major urban area with three sets of labels: land use, building function, and
building age. We find that our approach is more accurate for all tasks, in some
cases dramatically so.
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
Authors: Xuebin Qin, Shida He, Camilo Perez Quintero, Abhineet Singh, Masood Dehghan, Martin Jagersand
Date: 2017-04-30 00:00:00
Tasks: Line Detection, Visual Tracking

This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking problem is tackled by identifying a subset of these line
segments and connecting them sequentially to form a closed boundary with the
largest saliency and a certain similarity to the previous one. Specifically, we
define a new tracking criterion which combines a grouping cost and an area
similarity constraint. The proposed criterion makes the resulting boundary
tracking more robust to local minima. To achieve real-time tracking
performance, we use Delaunay Triangulation to build a graph model with the
detected line segments and then reduce the tracking problem to finding the
optimal cycle in this graph. This is solved by our newly proposed closed
boundary candidates searching algorithm called "Bidirectional Shortest Path
(BDSP)". The efficiency and robustness of the proposed method are tested on
real video sequences as well as during a robot arm pouring experiment.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
Authors: Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
Date: 2017-05-04 00:00:00
Tasks: Binary Classification, General Classification, Noise Estimation, Robust classification

Noisy PN learning is the problem of binary classification when training
examples may be mislabeled (flipped) uniformly with noise rate rho1 for
positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
to solve noisy PN learning and the open problem of estimating the noise rates,
i.e. the fraction of wrong positive and negative labels. Unlike prior
solutions, RP is time-efficient and general, requiring O(T) for any
unrestricted choice of probabilistic classifier with T fitting time. We prove
RP has consistent noise estimation and equivalent expected risk as learning
with uncorrupted labels in ideal conditions, and derive closed-form solutions
when conditions are non-ideal. RP achieves state-of-the-art noise estimation
and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the
amount of noise and performs similarly impressively when a large portion of
training examples are noise drawn from a third distribution. To highlight, RP
with a CNN classifier can predict if an MNIST digit is a "one"or "not" with
only 0.25% error, and 0.46 error across all digits, even when 50% of positive
examples are mislabeled and 50% of observed positive labels are mislabeled
negative examples.
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications
Authors: Rongrong Zhang, Wei Deng, Michael Yu Zhu
Date: 2017-08-09 00:00:00
Tasks: Model Selection, parameter estimation

Statistical analysis (SA) is a complex process to deduce population
properties from analysis of data. It usually takes a well-trained analyst to
successfully perform SA, and it becomes extremely challenging to apply SA to
big data applications. We propose to use deep neural networks to automate the
SA process. In particular, we propose to construct convolutional neural
networks (CNNs) to perform automatic model selection and parameter estimation,
two most important SA tasks. We refer to the resulting CNNs as the neural model
selector and the neural model estimator, respectively, which can be properly
trained using labeled data systematically generated from candidate models.
Simulation study shows that both the selector and estimator demonstrate
excellent performances. The idea and proposed framework can be further extended
to automate the entire SA process and have the potential to revolutionize how
SA is performed in big data analytics.
Addendum to: Summary Information for Reasoning About Hierarchical Plans
Authors: Lavindra de Silva, Sebastian Sardina, Lin Padgham
Date: 2017-08-09 00:00:00

Hierarchically structured agent plans are important for efficient planning
and acting, and they also serve (among other things) to produce "richer"
classical plans, composed not just of a sequence of primitive actions, but also
"abstract" ones representing the supplied hierarchies. A crucial step for this
and other approaches is deriving precondition and effect "summaries" from a
given plan hierarchy. This paper provides mechanisms to do this for more
pragmatic and conventional hierarchies than in the past. To this end, we
formally define the notion of a precondition and an effect for a hierarchical
plan; we present data structures and algorithms for automatically deriving this
information; and we analyse the properties of the presented algorithms. We
conclude the paper by detailing how our algorithms may be used together with a
classical planner in order to obtain abstract plans.
Identifying Reference Spans: Topic Modeling and Word Embeddings help IR
Authors: Luis Moraes, Shahryar Baki, Rakesh Verma, Daniel Lee
Date: 2017-08-09 00:00:00
Tasks: Topic Models, Word Embeddings

The CL-SciSumm 2016 shared task introduced an interesting problem: given a
document D and a piece of text that cites D, how do we identify the text spans
of D being referenced by the piece of text? The shared task provided the first
annotated dataset for studying this problem. We present an analysis of our
continued work in improving our system's performance on this task. We
demonstrate how topic models and word embeddings can be used to surpass the
previously best performing system.