pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Identifying Metastatic Breast Cancer
Authors: Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H. Beck
Date: 2016-06-18 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, whole slide images

The International Symposium on Biomedical Imaging (ISBI) held a grand
challenge to evaluate computational systems for the automated detection of
metastatic breast cancer in whole slide images of sentinel lymph node biopsies.
Our team won both competitions in the grand challenge, obtaining an area under
the receiver operating curve (AUC) of 0.925 for the task of whole slide image
classification and a score of 0.7051 for the tumor localization task. A
pathologist independently reviewed the same images, obtaining a whole slide
image classification AUC of 0.966 and a tumor localization score of 0.733.
Combining our deep learning system's predictions with the human pathologist's
diagnoses increased the pathologist's AUC to 0.995, representing an
approximately 85 percent reduction in human error rate. These results
demonstrate the power of using deep learning to produce significant
improvements in the accuracy of pathological diagnoses.
Scalable Greedy Algorithms for Transfer Learning
Authors: Ilja Kuzborskij, Francesco Orabona, Barbara Caputo
Date: 2014-08-06 00:00:00
Tasks: feature selection, Transfer Learning

In this paper we consider the binary transfer learning problem, focusing on
how to select and combine sources from a large pool to yield a good performance
on a target task. Constraining our scenario to real world, we do not assume the
direct access to the source data, but rather we employ the source hypotheses
trained from them. We propose an efficient algorithm that selects relevant
source hypotheses and feature dimensions simultaneously, building on the
literature on the best subset selection problem. Our algorithm achieves
state-of-the-art results on three computer vision datasets, substantially
outperforming both transfer learning and popular feature selection baselines in
a small-sample setting. We also present a randomized variant that achieves the
same results with the computational cost independent from the number of source
hypotheses and feature dimensions. Also, we theoretically prove that, under
reasonable assumptions on the source hypotheses, our algorithm can learn
effectively from few examples.
Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon
Authors: Lu Wang, Claire Cardie
Date: 2016-06-17 00:00:00
Proceeding: WS 2014 6
Tasks: Sentence

We study the problem of agreement and disagreement detection in online
discussions. An isotonic Conditional Random Fields (isotonic CRF) based
sequential model is proposed to make predictions on sentence- or segment-level.
We automatically construct a socially-tuned lexicon that is bootstrapped from
existing general-purpose sentiment lexicons to further improve the performance.
We evaluate our agreement and disagreement tagging model on two disparate
online discussion corpora -- Wikipedia Talk pages and online debates. Our model
is shown to outperform the state-of-the-art approaches in both datasets. For
example, the isotonic CRF model achieves F1 scores of 0.74 and 0.67 for
agreement and disagreement detection, when a linear chain CRF obtains 0.58 and
0.56 for the discussions on Wikipedia Talk pages.
Strategies for Searching Video Content with Text Queries or Video Examples
Authors: Shoou-I Yu, Yi Yang, Zhongwen Xu, Shicheng Xu, Deyu Meng, Zexi Mao, Zhigang Ma, Ming Lin, Xuanchong Li, Huan Li, Zhenzhong Lan, Lu Jiang, Alexander G. Hauptmann, Chuang Gan, Xingzhong Du, Xiaojun Chang
Date: 2016-06-17 00:00:00
Tasks: Event Detection, Reranking, Retrieval, Video Retrieval

The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches.
A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection
Authors: Lu Wang, Claire Cardie
Date: 2016-06-17 00:00:00
Proceeding: ACL 2014 6
Tasks: Sentence, Sentiment Analysis

We investigate the novel task of online dispute detection and propose a
sentiment analysis solution to the problem: we aim to identify the sequence of
sentence-level sentiments expressed during a discussion and to use them as
features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the
discussion as a whole. We evaluate dispute detection approaches on a newly
created corpus of Wikipedia Talk page disputes and find that classifiers that
rely on our sentiment tagging features outperform those that do not. The best
model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.
A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model
Authors: Joan Duran, Antoni Buades, Bartomeu Coll, Catalina Sbert, Gwendoline Blanchet
Date: 2016-06-17 00:00:00
Tasks: Pansharpening

Most satellites decouple the acquisition of a panchromatic image at high
spatial resolution from the acquisition of a multispectral image at lower
spatial resolution. Pansharpening is a fusion technique used to increase the
spatial resolution of the multispectral data while simultaneously preserving
its spectral information. In this paper, we consider pansharpening as an
optimization problem minimizing a cost function with a nonlocal regularization
term. The energy functional which is to be minimized decouples for each band,
thus permitting the application to misregistered spectral components. This
requirement is achieved by dropping the, commonly used, assumption that relates
the spectral and panchromatic modalities by a linear transformation. Instead, a
new constraint that preserves the radiometric ratio between the panchromatic
and each spectral component is introduced. An exhaustive performance comparison
of the proposed fusion method with several classical and state-of-the-art
pansharpening techniques illustrates its superiority in preserving spatial
details, reducing color distortions, and avoiding the creation of aliasing
artifacts.
Query-Focused Opinion Summarization for User-Generated Content
Authors: Lu Wang, Hema Raghavan, Claire Cardie, Vittorio Castelli
Date: 2016-06-17 00:00:00
Proceeding: COLING 2014 8
Tasks: Diversity, Opinion Summarization, text similarity

We present a submodular function-based framework for query-focused opinion
summarization. Within our framework, relevance ordering produced by a
statistical ranker, and information coverage with respect to topic distribution
and diverse viewpoints are both encoded as submodular functions. Dispersion
functions are utilized to minimize the redundancy. We are the first to evaluate
different metrics of text similarity for submodularity-based summarization
methods. By experimenting on community QA and blog summarization, we show that
our system outperforms state-of-the-art approaches in both automatic evaluation
and human evaluation. A human evaluation task is conducted on Amazon Mechanical
Turk with scale, and shows that our systems are able to generate summaries of
high overall quality and information diversity.
Why Regularized Auto-Encoders learn Sparse Representation?
Authors: Devansh Arpit, Yingbo Zhou, Hung Ngo, Venu Govindaraju
Date: 2015-05-21 00:00:00

While the authors of Batch Normalization (BN) identify and address an
important problem involved in training deep networks-- \textit{Internal
Covariate Shift}-- the current solution has certain drawbacks. For instance, BN
depends on batch statistics for layerwise input normalization during training
which makes the estimates of mean and standard deviation of input
(distribution) to hidden layers inaccurate due to shifting parameter values
(especially during initial training epochs). Another fundamental problem with
BN is that it cannot be used with batch-size $ 1 $ during training. We address
these drawbacks of BN by proposing a non-adaptive normalization technique for
removing covariate shift, that we call \textit{Normalization Propagation}. Our
approach does not depend on batch statistics, but rather uses a
data-independent parametric estimate of mean and standard-deviation in every
layer thus being computationally faster compared with BN. We exploit the
observation that the pre-activation before Rectified Linear Units follow
Gaussian distribution in deep networks, and that once the first and second
order statistics of any given dataset are normalized, we can forward propagate
this normalization without the need for recalculating the approximate
statistics for hidden layers.
Socially-Informed Timeline Generation for Complex Events
Authors: Lu Wang, Claire Cardie, Galen Marchetti
Date: 2016-06-17 00:00:00
Proceeding: HLT 2015 5
Tasks: Informativeness

Existing timeline generation systems for complex events consider only
information from traditional media, ignoring the rich social context provided
by user-generated content that reveals representative public interests or
insightful opinions. We instead aim to generate socially-informed timelines
that contain both news article summaries and selected user comments. We present
an optimization framework designed to balance topical cohesion between the
article and comment summaries along with their informativeness and coverage of
the event. Automatic evaluations on real-world datasets that cover four complex
events show that our system produces more informative timelines than
state-of-the-art systems. In human evaluation, the associated comment summaries
are furthermore rated more insightful than editor's picks and comments ranked
highly by users.
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Authors: Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy
Date: 2016-06-17 00:00:00
Proceeding: SEMEVAL 2016 6
Tasks: Data Augmentation, Text Categorization

This paper describes our approach for the Detecting Stance in Tweets task
(SemEval-2016 Task 6). We utilized recent advances in short text categorization
using deep learning to create word-level and character-level models. The choice
between word-level and character-level models in each particular case was
informed through validation performance. Our final system is a combination of
classifiers using word-level or character-level models. We also employed novel
data augmentation techniques to expand and diversify our training dataset, thus
making our system more robust. Our system achieved a macro-average precision,
recall and F1-scores of 0.67, 0.61 and 0.635 respectively.
Structured Stochastic Linear Bandits
Authors: Nicholas Johnson, Vidyashankar Sivakumar, Arindam Banerjee
Date: 2016-06-17 00:00:00

The stochastic linear bandit problem proceeds in rounds where at each round
the algorithm selects a vector from a decision set after which it receives a
noisy linear loss parameterized by an unknown vector. The goal in such a
problem is to minimize the (pseudo) regret which is the difference between the
total expected loss of the algorithm and the total expected loss of the best
fixed vector in hindsight. In this paper, we consider settings where the
unknown parameter has structure, e.g., sparse, group sparse, low-rank, which
can be captured by a norm, e.g., $L_1$, $L_{(1,2)}$, nuclear norm. We focus on
constructing confidence ellipsoids which contain the unknown parameter across
all rounds with high-probability. We show the radius of such ellipsoids depend
on the Gaussian width of sets associated with the norm capturing the structure.
Such characterization leads to tighter confidence ellipsoids and, therefore,
sharper regret bounds compared to bounds in the existing literature which are
based on the ambient dimensionality.
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs
Authors: Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung
Date: 2016-06-17 00:00:00
Tasks: CPU, GPU, Image Segmentation, object-detection, Object Detection, Semantic Segmentation

Sliding window convolutional networks (ConvNets) have become a popular
approach to computer vision problems such as image segmentation, and object
detection and localization. Here we consider the problem of inference, the
application of a previously trained ConvNet, with emphasis on 3D images. Our
goal is to maximize throughput, defined as average number of output voxels
computed per unit time. Other things being equal, processing a larger image
tends to increase throughput, because fractionally less computation is wasted
on the borders of the image. It follows that an apparently slower algorithm may
end up having higher throughput if it can process a larger image within the
constraint of the available RAM. We introduce novel CPU and GPU primitives for
convolutional and pooling layers, which are designed to minimize memory
overhead. The primitives include convolution based on highly efficient pruned
FFTs. Our theoretical analyses and empirical tests reveal a number of
interesting findings. For some ConvNet architectures, cuDNN is outperformed by
our FFT-based GPU primitives, and these in turn can be outperformed by our CPU
primitives. The CPU manages to achieve higher throughput because of its fast
access to more RAM. A novel primitive in which the GPU accesses host RAM can
significantly increase GPU throughput. Finally, a CPU-GPU algorithm achieves
the greatest throughput of all, 10x or more than other publicly available
implementations of sliding window 3D ConvNets. All of our code has been made
available as open source project.
Hierarchical Data Generator based on Tree-Structured Stick Breaking Process for Benchmarking Clustering Methods
Authors: Łukasz P. Olech, Michał Spytkowski, Halina Kwaśnicka, Zbigniew Michalewicz
Date: 2016-06-17 00:00:00
Tasks: Benchmarking, Clustering, Object

Object Cluster Hierarchies is a new variant of Hierarchical Cluster Analysis that gains interest in the field of Machine Learning. Being still at an early stage of development, the lack of tools for systematic analysis of Object Cluster Hierarchies inhibits its further improvement. In this paper we address this issue by proposing a generator of synthetic hierarchical data that can be used for benchmarking Object Cluster Hierarchy methods. The article presents a thorough empirical and theoretical analysis of the generator and provides guidance on how to control its parameters. Conducted experiments show the usefulness of the data generator that is capable of producing a wide range of differently structured data. Further, benchmarking datasets that mirror the most common types of hierarchies are generated and made available to the public, together with the developed generator (http://kio.pwr.edu.pl/?page\_id=396).
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
Authors: Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod Vokkarane, Yunsheng Ma
Date: 2016-06-17 00:00:00
Tasks: Cloud Computing, Fine-Grained Image Recognition

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were
overweight. Of these, over 600 million were obese. Accurately documenting
dietary caloric intake is crucial to manage weight loss, but also presents
challenges because most of the current methods for dietary assessment must rely
on memory to recall foods eaten. The ultimate goal of our research is to
develop computer-aided technical solutions to enhance and improve the accuracy
of current measurements of dietary intake. Our proposed system in this paper
aims to improve the accuracy of dietary assessment by analyzing the food images
captured by mobile devices (e.g., smartphone). The key technique innovation in
this paper is the deep learning-based food image recognition algorithms.
Substantial research has demonstrated that digital imaging accurately estimates
dietary intake in many environments and it has many advantages over other
methods. However, how to derive the food information (e.g., food type and
portion size) from food image effectively and efficiently remains a challenging
and open research problem. We propose a new Convolutional Neural Network
(CNN)-based food image recognition algorithm to address this problem. We
applied our proposed approach to two real-world food image data sets (UEC-256
and Food-101) and achieved impressive results. To the best of our knowledge,
these results outperformed all other reported work using these two data sets.
Our experiments have demonstrated that the proposed approach is a promising
solution for addressing the food image recognition problem. Our future work
includes further improving the performance of the algorithms and integrating
our system into a real-world mobile and cloud computing-based system to enhance
the accuracy of current measurements of dietary intake.
Interpretability in Linear Brain Decoding
Authors: Seyed Mostafa Kia, Andrea Passerini
Date: 2016-06-17 00:00:00
Tasks: Brain Decoding, Model Selection

Improving the interpretability of brain decoding approaches is of primary
interest in many neuroimaging studies. Despite extensive studies of this type,
at present, there is no formal definition for interpretability of brain
decoding models. As a consequence, there is no quantitative measure for
evaluating the interpretability of different brain decoding methods. In this
paper, we present a simple definition for interpretability of linear brain
decoding models. Then, we propose to combine the interpretability and the
performance of the brain decoding into a new multi-objective criterion for
model selection. Our preliminary results on the toy data show that optimizing
the hyper-parameters of the regularized linear classifier based on the proposed
criterion results in more informative linear models. The presented definition
provides the theoretical background for quantitative evaluation of
interpretability in linear brain decoding.
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Authors: Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko
Date: 2015-11-20 00:00:00
Tasks: Hyperparameter Optimization

Hyperparameter selection generally relies on running multiple full training
trials, with selection based on validation set performance. We propose a
gradient-based approach for locally adjusting hyperparameters during training
of the model. Hyperparameters are adjusted so as to make the model parameter
gradients, and hence updates, more advantageous for the validation cost. We
explore the approach for tuning regularization hyperparameters and find that in
experiments on MNIST, SVHN and CIFAR-10, the resulting regularization levels
are within the optimal regions. The additional computational cost depends on
how frequently the hyperparameters are trained, but the tested scheme adds only
30% computational overhead regardless of the model size. Since the method is
significantly less computationally demanding compared to similar gradient-based
approaches to hyperparameter optimization, and consistently finds good
hyperparameter values, it can be a useful tool for training neural network
models.
Multi-View Treelet Transform
Authors: Brian A. Mitchell, Linda R. Petzold
Date: 2016-06-02 00:00:00
Tasks: Denoising

Current multi-view factorization methods make assumptions that are not
acceptable for many kinds of data, and in particular, for graphical data with
hierarchical structure. At the same time, current hierarchical methods work
only in the single-view setting. We generalize the Treelet Transform to the
Multi-View Treelet Transform (MVTT) to allow for the capture of hierarchical
structure when multiple views are available. Further, we show how this
generalization is consistent with the existing theory and how it might be used
in denoising empirical networks and in computing the shared response of
functional brain data.
High-resolution LIDAR-based Depth Mapping using Bilateral Filter
Authors: C. Premebida, L. Garrote, A. Asvadi, A. Pedro Ribeiro, U. Nunes
Date: 2016-06-17 00:00:00
Tasks: object-detection, Object Detection, Semantic Segmentation, Spatial Interpolation, Vocal Bursts Intensity Prediction

High resolution depth-maps, obtained by upsampling sparse range data from a
3D-LIDAR, find applications in many fields ranging from sensory perception to
semantic segmentation and object detection. Upsampling is often based on
combining data from a monocular camera to compensate the low-resolution of a
LIDAR. This paper, on the other hand, introduces a novel framework to obtain
dense depth-map solely from a single LIDAR point cloud; which is a research
direction that has been barely explored. The formulation behind the proposed
depth-mapping process relies on local spatial interpolation, using
sliding-window (mask) technique, and on the Bilateral Filter (BF) where the
variable of interest, the distance from the sensor, is considered in the
interpolation problem. In particular, the BF is conveniently modified to
perform depth-map upsampling such that the edges (foreground-background
discontinuities) are better preserved by means of a proposed method which
influences the range-based weighting term. Other methods for spatial upsampling
are discussed, evaluated and compared in terms of different error measures.
This paper also researches the role of the mask's size in the performance of
the implemented methods. Quantitative and qualitative results from experiments
on the KITTI Database, using LIDAR point clouds only, show very satisfactory
performance of the approach introduced in this work.
Exponential expressivity in deep neural networks through transient chaos
Authors: Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12

We combine Riemannian geometry with the mean field theory of high dimensional
chaos to study the nature of signal propagation in generic, deep neural
networks with random weights. Our results reveal an order-to-chaos expressivity
phase transition, with networks in the chaotic phase computing nonlinear
functions whose global curvature grows exponentially with depth but not width.
We prove this generic class of deep random functions cannot be efficiently
computed by any shallow network, going beyond prior work restricted to the
analysis of single functions. Moreover, we formalize and quantitatively
demonstrate the long conjectured idea that deep networks can disentangle highly
curved manifolds in input space into flat manifolds in hidden space. Our
theoretical analysis of the expressive power of deep networks broadly applies
to arbitrary nonlinearities, and provides a quantitative underpinning for
previously abstract notions about the geometry of deep functions.