pwc-archive/papers-with-abstracts huggingface dataset:
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Authors: Xu Han, Roland Kwitt, Stephen Aylward, Spyridon Bakas, Bjoern Menze, Alexander Asturias, Paul Vespa, John Van Horn, Marc Niethammer
Date: 2017-11-15 00:00:00
Tasks: Image Reconstruction, Image Registration

Brain extraction from images is a common pre-processing step. Many approaches
exist, but they are frequently only designed to perform brain extraction from
images without strong pathologies. Extracting the brain from images with strong
pathologies, for example, the presence of a tumor or of a traumatic brain
injury, is challenging. In such cases, tissue appearance may deviate from
normal tissue and violates algorithmic assumptions for these approaches; hence,
the brain may not be correctly extracted. This paper proposes a brain
extraction approach which can explicitly account for pathologies by jointly
modeling normal tissue and pathologies. Specifically, our model uses a
three-part image decomposition: (1) normal tissue appearance is captured by
principal component analysis, (2) pathologies are captured via a total
variation term, and (3) non-brain tissue is captured by a sparse term.
Decomposition and image registration steps are alternated to allow statistical
modeling in a fixed atlas space. As a beneficial side effect, the model allows
for the identification of potential pathologies and the reconstruction of a
quasi-normal image in atlas space. We demonstrate the effectiveness of our
method on four datasets: the IBSR and LPBA40 datasets which show normal images,
the BRATS dataset containing images with brain tumors and a dataset containing
clinical TBI images. We compare the performance with other popular models:
ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach.
Our model performs better than these competing methods on all four datasets.
Specifically, our model achieves the best median (97.11) and mean (96.88) Dice
scores over all datasets. The two best performing competitors, ROBEX and MASS,
achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach
is an effective method for high quality brain extraction on a wide variety of
images.
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks
Authors: Dushyanta Dhyani
Date: 2018-02-25 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Data Augmentation, General Classification, Relation Classification, Relation Extraction

We describe our system for SemEval-2018 Shared Task on Semantic Relation
Extraction and Classification in Scientific Papers where we focus on the
Classification task. Our simple piecewise convolution neural encoder performs
decently in an end to end manner. A simple inter-task data augmentation
signifi- cantly boosts the performance of the model. Our best-performing
systems stood 8th out of 20 teams on the classification task on noisy data and
12th out of 28 teams on the classification task on clean data.
Randomized Nonnegative Matrix Factorization
Authors: N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz
Date: 2017-11-06 00:00:00

Nonnegative matrix factorization (NMF) is a powerful tool for data mining.
However, the emergence of `big data' has severely challenged our ability to
compute this fundamental decomposition using deterministic algorithms. This
paper presents a randomized hierarchical alternating least squares (HALS)
algorithm to compute the NMF. By deriving a smaller matrix from the nonnegative
input data, a more efficient nonnegative decomposition can be computed. Our
algorithm scales to big data applications while attaining a near-optimal
factorization. The proposed algorithm is evaluated using synthetic and real
world data and shows substantial speedups compared to deterministic HALS.
Horizontal Pyramid Matching for Person Re-identification
Authors: Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, Thomas Huang
Date: 2018-04-14 00:00:00
Tasks: Person Re-Identification

Despite the remarkable recent progress, person re-identification (Re-ID)
approaches are still suffering from the failure cases where the discriminative
body parts are missing. To mitigate such cases, we propose a simple yet
effective Horizontal Pyramid Matching (HPM) approach to fully exploit various
partial information of a given person, so that correct person candidates can be
still identified even even some key parts are missing. Within the HPM, we make
the following contributions to produce a more robust feature representation for
the Re-ID task: 1) we learn to classify using partial feature representations
at different horizontal pyramid scales, which successfully enhance the
discriminative capabilities of various person parts; 2) we exploit average and
max pooling strategies to account for person-specific discriminative
information in a global-local manner. To validate the effectiveness of the
proposed HPM, extensive experiments are conducted on three popular benchmarks,
including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP
scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new
state-of-the-arts. Our code is available on Github
A General Framework for Counterfactual Learning-to-Rank
Authors: Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims
Date: 2018-04-30 00:00:00
Tasks: counterfactual, Counterfactual Inference, Learning-To-Rank

Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias. For the special case of optimizing average rank for linear ranking functions, however, the recently developed SVM-PropRank method has shown that counterfactual inference techniques can be used to provably overcome the distorting effect of presentation bias. Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables unbiased training for a broad class of additive ranking metrics (e.g., Discounted Cumulative Gain (DCG)) as well as a broad class of models (e.g., deep networks). Specifically, we derive a relaxation for propensity-weighted rank-based metrics which is subdifferentiable and thus suitable for gradient-based optimization. We demonstrate the effectiveness of this general approach by instantiating two new learning methods. One is a new type of unbiased SVM that optimizes DCG -- called SVM PropDCG --, and we show how the resulting optimization problem can be solved via the Convex Concave Procedure (CCP). The other is Deep PropDCG, where the ranking function can be an arbitrary deep network. In addition to the theoretical support, we empirically find that SVM PropDCG significantly outperforms existing linear rankers in terms of DCG. Moreover, the ability to train non-linear ranking functions via Deep PropDCG further improves performance.
A Portuguese Native Language Identification Dataset
Authors: Iria del Río, Marcos Zampieri, Shervin Malmasi
Date: 2018-04-30 00:00:00
Proceeding: WS 2018 6
Tasks: Language Acquisition, Language Identification, Native Language Identification, POS

In this paper we present NLI-PT, the first Portuguese dataset compiled for
Native Language Identification (NLI), the task of identifying an author's first
language based on their second language writing. The dataset includes 1,868
student essays written by learners of European Portuguese, native speakers of
the following L1s: Chinese, English, Spanish, German, Russian, French,
Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish.
NLI-PT includes the original student text and four different types of
annotation: POS, fine-grained POS, constituency parses, and dependency parses.
NLI-PT can be used not only in NLI but also in research on several topics in
the field of Second Language Acquisition and educational NLP. We discuss
possible applications of this dataset and present the results obtained for the
first lexical baseline system for Portuguese NLI.
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
Authors: Maria-Florina Balcan, Travis Dick, Ellen Vitercik
Date: 2017-11-08 00:00:00

Data-driven algorithm design, that is, choosing the best algorithm for a
specific application, is a crucial problem in modern data science.
Practitioners often optimize over a parameterized algorithm family, tuning
parameters based on problems from their domain. These procedures have
historically come with no guarantees, though a recent line of work studies
algorithm selection from a theoretical perspective. We advance the foundations
of this field in several directions: we analyze online algorithm selection,
where problems arrive one-by-one and the goal is to minimize regret, and
private algorithm selection, where the goal is to find good parameters over a
set of problems without revealing sensitive information contained therein. We
study important algorithm families, including SDP-rounding schemes for problems
formulated as integer quadratic programs, and greedy techniques for canonical
subset selection problems. In these cases, the algorithm's performance is a
volatile and piecewise Lipschitz function of its parameters, since tweaking the
parameters can completely change the algorithm's behavior. We give a sufficient
and general condition, dispersion, defining a family of piecewise Lipschitz
functions that can be optimized online and privately, which includes the
functions measuring the performance of the algorithms we study. Intuitively, a
set of piecewise Lipschitz functions is dispersed if no small region contains
many of the functions' discontinuities. We present general techniques for
online and private optimization of the sum of dispersed piecewise Lipschitz
functions. We improve over the best-known regret bounds for a variety of
problems, prove regret bounds for problems not previously studied, and give
matching lower bounds. We also give matching upper and lower bounds on the
utility loss due to privacy. Moreover, we uncover dispersion in auction design
and pricing problems.
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Authors: Chunhui Gu, Chen Sun, David A. Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra Malik
Date: 2017-05-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Actin Detection, Action Detection, Action Localization, Action Recognition, Spatio-temporal Action Recognition, Temporal Action Localization, Video Understanding

This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
  AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.
Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications
Authors: Baohua Sun, Lin Yang, Patrick Dong, Wenhan Zhang, Jason Dong, Charles Young
Date: 2018-04-30 00:00:00

Computer vision performances have been significantly improved in recent years
by Convolutional Neural Networks(CNN). Currently, applications using CNN
algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs
or FPGAs. However, power consumption, speed, accuracy, memory footprint, and
die size should all be taken into consideration for mobile and embedded
applications. Domain Specific Architecture (DSA) for CNN is the efficient and
practical solution for CNN deployment and implementation. We designed and
produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra
power-efficient performance of 9.3TOPS/Watt and with all processing done in the
internal memory instead of outside DRAM. It classifies 224x224 RGB image inputs
at more than 140fps with peak power consumption at less than 300mW and an
accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is
reconfigurable to support CNN model coefficients of various layer sizes and
layer types, including convolution, depth-wise convolution, short-cut
connections, max pooling, and ReLU. Furthermore, in order to better support
real-world deployment for various application scenarios, especially with
low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also
designed algorithms to fully utilize the CNN-DSA accelerator efficiently by
reducing the dependency on external accelerator computation resources,
including implementation of Fully-Connected (FC) layers within the accelerator
and compression of extracted features from the CNN-DSA accelerator. Live demos
with our CNN-DSA accelerator on mobile and embedded systems show its
capabilities to be widely and practically applied in the real world.
On the iterative refinement of densely connected representation levels for semantic segmentation
Authors: Arantxa Casanova, Guillem Cucurull, Michal Drozdzal, Adriana Romero, Yoshua Bengio
Date: 2018-04-30 00:00:00
Tasks: Image Segmentation, Scene Understanding, Semantic Segmentation

State-of-the-art semantic segmentation approaches increase the receptive
field of their models by using either a downsampling path composed of
poolings/strided convolutions or successive dilated convolutions. However, it
is not clear which operation leads to best results. In this paper, we
systematically study the differences introduced by distinct receptive field
enlargement methods and their impact on the performance of a novel
architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a
densely connected backbone composed of residual networks. Following standard
image segmentation architectures, receptive field enlargement operations that
change the representation level are interleaved among residual networks. This
allows the model to exploit the benefits of both residual and dense
connectivity patterns, namely: gradient flow, iterative refinement of
representations, multi-scale feature combination and deep supervision. In order
to highlight the potential of our model, we test it on the challenging CamVid
urban scene understanding benchmark and make the following observations: 1)
downsampling operations outperform dilations when the model is trained from
scratch, 2) dilations are useful during the finetuning step of the model, 3)
coarser representations require less refinement steps, and 4) ResNets (by model
construction) are good regularizers, since they can reduce the model capacity
when needed. Finally, we compare our architecture to alternative methods and
report state-of-the-art result on the Camvid dataset, with at least twice fewer
parameters.
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Authors: Gonzalo Iglesias, William Tambellini, Adrià De Gispert, Eva Hasler, Bill Byrne
Date: 2018-04-30 00:00:00
Proceeding: NAACL 2018 6
Tasks: NMT

We describe a batched beam decoding algorithm for NMT with LMBR n-gram
posteriors, showing that LMBR techniques still yield gains on top of the best
recently reported results with Transformers. We also discuss acceleration
strategies for deployment, and the effect of the beam size and batching on
memory and speed.
Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos
Authors: Gunnar A. Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, Karteek Alahari
Date: 2018-04-25 00:00:00
Tasks: General Classification, Video Classification, Video Understanding

In Actor and Observer we introduced a dataset linking the first and
third-person video understanding domains, the Charades-Ego Dataset. In this
paper we describe the egocentric aspect of the dataset and present annotations
for Charades-Ego with 68,536 activity instances in 68.8 hours of first and
third-person video, making it one of the largest and most diverse egocentric
datasets available. Charades-Ego furthermore shares activity classes, scripts,
and methodology with the Charades dataset, that consist of additional 82.3
hours of third-person video with 66,500 activity instances. Charades-Ego has
temporal annotations and textual descriptions, making it suitable for
egocentric video classification, localization, captioning, and new tasks
utilizing the cross-modal nature of the data.
Hybrid Forests for Left Ventricle Segmentation using only the first slice label
Authors: Ismaël Koné, Lahsen Boulmane
Date: 2018-04-30 00:00:00
Tasks: Left Ventricle Segmentation, Segmentation

Machine learning models produce state-of-the-art results in many MRI images
segmentation. However, most of these models are trained on very large datasets
which come from experts manual labeling. This labeling process is very time
consuming and costs experts work. Therefore finding a way to reduce this cost
is on high demand. In this paper, we propose a segmentation method which
exploits MRI images sequential structure to nearly drop out this labeling task.
Only the first slice needs to be manually labeled to train the model which then
infers the next slice's segmentation. Inference result is another datum used to
train the model again. The updated model then infers the third slice and the
same process is carried out until the last slice. The proposed model is an
combination of two Random Forest algorithms: the classical one and a recent one
namely Mondrian Forests. We applied our method on human left ventricle
segmentation and results are very promising. This method can also be used to
generate labels.
Visual Tracking via Dynamic Graph Learning
Authors: Chenglong Li, Liang Lin, WangMeng Zuo, Jin Tang, Ming-Hsuan Yang
Date: 2017-10-04 00:00:00
Tasks: Graph Learning, Object, Object Tracking, Visual Tracking

Existing visual tracking methods usually localize a target object with a
bounding box, in which the performance of the foreground object trackers or
detectors is often affected by the inclusion of background clutter. To handle
this problem, we learn a patch-based graph representation for visual tracking.
The tracked object is modeled by with a graph by taking a set of
non-overlapping image patches as nodes, in which the weight of each node
indicates how likely it belongs to the foreground and edges are weighted for
indicating the appearance compatibility of two neighboring nodes. This graph is
dynamically learned and applied in object tracking and model updating. During
the tracking process, the proposed algorithm performs three main steps in each
frame. First, the graph is initialized by assigning binary weights of some
image patches to indicate the object and background patches according to the
predicted bounding box. Second, the graph is optimized to refine the patch
weights by using a novel alternating direction method of multipliers. Third,
the object feature representation is updated by imposing the weights of patches
on the extracted image features. The object location is predicted by maximizing
the classification score in the structured support vector machine. Extensive
experiments show that the proposed tracking algorithm performs well against the
state-of-the-art methods on large-scale benchmark datasets.
How Robust are Deep Neural Networks?
Authors: Biswa Sengupta, Karl J. Friston
Date: 2018-04-30 00:00:00
Tasks: Reinforcement Learning

Convolutional and Recurrent, deep neural networks have been successful in
machine learning systems for computer vision, reinforcement learning, and other
allied fields. However, the robustness of such neural networks is seldom
apprised, especially after high classification accuracy has been attained. In
this paper, we evaluate the robustness of three recurrent neural networks to
tiny perturbations, on three widely used datasets, to argue that high accuracy
does not always mean a stable and a robust (to bounded perturbations,
adversarial attacks, etc.) system. Especially, normalizing the spectrum of the
discrete recurrent network to bound the spectrum (using power method, Rayleigh
quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural
networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that
training of recurrent networks, say using gradient-based methods, often result
in non-normal matrices that may or may not be diagonalizable. Therefore, the
open problem lies in constructing methods that optimize not only for accuracy
but also for the stability and the robustness of the underlying neural network,
a criterion that is distinct from the other.
Sampling strategies in Siamese Networks for unsupervised speech representation learning
Authors: Rachid Riad, Corentin Dancette, Julien Karadayi, Neil Zeghidour, Thomas Schatz, Emmanuel Dupoux
Date: 2018-04-30 00:00:00
Tasks: Representation Learning, Speech Representation Learning

Recent studies have investigated siamese network architectures for learning
invariant speech representations using same-different side information at the
word level. Here we investigate systematically an often ignored component of
siamese networks: the sampling procedure (how pairs of same vs. different
tokens are selected). We show that sampling strategies taking into account
Zipf's Law, the distribution of speakers and the proportions of same and
different pairs of words significantly impact the performance of the network.
In particular, we show that word frequency compression improves learning across
a large range of variations in number of training pairs. This effect does not
apply to the same extent to the fully unsupervised setting, where the pairs of
same-different words are obtained by spoken term discovery. We apply these
results to pairs of words discovered using an unsupervised algorithm and show
an improvement on state-of-the-art in unsupervised representation learning
using siamese networks.
Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup
Authors: Artem Sevastopolsky, Stepan Drapak, Konstantin Kiselev, Blake M. Snyder, Jeremy D. Keenan, Anastasia Georgievskaya
Date: 2018-04-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this work, we propose a special cascade network for image segmentation,
which is based on the U-Net networks as building blocks and the idea of the
iterative refinement. The model was mainly applied to achieve higher
recognition quality for the task of finding borders of the optic disc and cup,
which are relevant to the presence of glaucoma. Compared to a single U-Net and
the state-of-the-art methods for the investigated tasks, very high segmentation
quality has been achieved without a need for increasing the volume of datasets.
Our experiments include comparison with the best-known methods on publicly
available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on a
private data set collected in collaboration with University of California San
Francisco Medical School. The analysis of the architecture details is
presented, and it is argued that the model can be employed for a broad scope of
image segmentation problems of similar nature.