pwc-archive/papers-with-abstracts huggingface dataset:
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
Authors: Mitko Veta, Paul J. van Diest, Josien P. W. Pluim
Date: 2016-06-20 00:00:00
Tasks: Segmentation

The size of nuclei in histological preparations from excised breast tumors is
predictive of patient outcome (large nuclei indicate poor outcome).
Pathologists take into account nuclear size when performing breast cancer
grading. In addition, the mean nuclear area (MNA) has been shown to have
independent prognostic value. The straightforward approach to measuring nuclear
size is by performing nuclei segmentation. We hypothesize that given an image
of a tumor region with known nuclei locations, the area of the individual
nuclei and region statistics such as the MNA can be reliably computed directly
from the image data by employing a machine learning model, without the
intermediate step of nuclei segmentation. Towards this goal, we train a deep
convolutional neural network model that is applied locally at each nucleus
location, and can reliably measure the area of the individual nuclei and the
MNA. Furthermore, we show how such an approach can be extended to perform
combined nuclei detection and measurement, which is reminiscent of
granulometry.
Quantifying and Reducing Stereotypes in Word Embeddings
Authors: Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai
Date: 2016-06-20 00:00:00
Tasks: Word Embeddings

Machine learning algorithms are optimized to model statistical properties of
the training data. If the input data reflects stereotypes and biases of the
broader society, then the output of the learning algorithm also captures these
stereotypes. In this paper, we initiate the study of gender stereotypes in {\em
word embedding}, a popular framework to represent text data. As their use
becomes increasingly common, applications can inadvertently amplify unwanted
stereotypes. We show across multiple datasets that the embeddings contain
significant gender stereotypes, especially with regard to professions. We
created a novel gender analogy task and combined it with crowdsourcing to
systematically quantify the gender bias in a given embedding. We developed an
efficient algorithm that reduces gender stereotype using just a handful of
training examples while preserving the useful geometric properties of the
embedding. We evaluated our algorithm on several metrics. While we focus on
male/female stereotypes, our framework may be applicable to other types of
embedding biases.
The Dichotomy for Conservative Constraint Satisfaction is Polynomially Decidable
Authors: Clément Carbonnel
Date: 2016-04-24 00:00:00

Given a fixed constraint language $\Gamma$, the conservative CSP over
$\Gamma$ (denoted by c-CSP($\Gamma$)) is a variant of CSP($\Gamma$) where the
domain of each variable can be restricted arbitrarily. A dichotomy is known for
conservative CSP: for every fixed language $\Gamma$, c-CSP($\Gamma$) is either
in P or NP-complete. However, the characterization of conservatively tractable
languages is of algebraic nature and the naive recognition algorithm is
super-exponential in the domain size. The main contribution of this paper is a
polynomial-time algorithm that, given a constraint language $\Gamma$ as input,
decides if c-CSP($\Gamma$) is tractable. In addition, if $\Gamma$ is proven
tractable the algorithm also outputs its coloured graph, which contains
valuable information on the structure of $\Gamma$.
Relative Natural Gradient for Learning Large Complex Models
Authors: Ke Sun, Frank Nielsen
Date: 2016-06-20 00:00:00

Fisher information and natural gradient provided deep insights and powerful
tools to artificial neural networks. However related analysis becomes more and
more difficult as the learner's structure turns large and complex. This paper
makes a preliminary step towards a new direction. We extract a local component
of a large neuron system, and defines its relative Fisher information metric
that describes accurately this small component, and is invariant to the other
parts of the system. This concept is important because the geometry structure
is much simplified and it can be easily applied to guide the learning of neural
networks. We provide an analysis on a list of commonly used components, and
demonstrate how to use this concept to further improve optimization.
Segmental Recurrent Neural Networks for End-to-end Speech Recognition
Authors: Liang Lu, Lingpeng Kong, Chris Dyer, Noah A. Smith, Steve Renals
Date: 2016-03-01 00:00:00
Tasks: Acoustic Modelling, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide features or segmentation boundaries. Instead, this model
marginalises out all the possible segmentations, and features are extracted
from the RNN trained together with the segmental CRF. In essence, this model is
self-contained and can be trained end-to-end. In this paper, we discuss
practical training and decoding issues as well as the method to speed up the
training in the context of speech recognition. We performed experiments on the
TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass
decoding --- the best reported result using CRFs, despite the fact that we only
used a zeroth-order CRF and without using any language model.
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance
Authors: Amr Bakry, Mohamed Elhoseiny, Tarek El-Gaaly, Ahmed Elgammal
Date: 2015-08-09 00:00:00

This paper is focused on studying the view-manifold structure in the feature
spaces implied by the different layers of Convolutional Neural Networks (CNN).
There are several questions that this paper aims to answer: Does the learned
CNN representation achieve viewpoint invariance? How does it achieve viewpoint
invariance? Is it achieved by collapsing the view manifolds, or separating them
while preserving them? At which layer is view invariance achieved? How can the
structure of the view manifold at each layer of a deep convolutional neural
network be quantified experimentally? How does fine-tuning of a pre-trained CNN
on a multi-view dataset affect the representation at each layer of the network?
In order to answer these questions we propose a methodology to quantify the
deformation and degeneracy of view manifolds in CNN layers. We apply this
methodology and report interesting results in this paper that answer the
aforementioned questions.
Contravening Esotery: Cryptanalysis of Knapsack Cipher using Genetic Algorithms
Authors: Harmeet Singh
Date: 2016-06-20 00:00:00
Tasks: Cryptanalysis

Cryptanalysis of knapsack cipher is a fascinating problem which has eluded
the computing fraternity for decades. However, in most of the cases either the
time complexity of the proposed algorithm is colossal or an insufficient number
of samples have been taken for verification. The present work proposes a
Genetic Algorithm based technique for cryptanalysis of knapsack cipher. The
experiments conducted prove the validity of the technique. The results prove
that the technique is better than the existing techniques. An extensive review
has been carried out in order to find the gaps in the existing techniques. The
work paves the way of the application of computational intelligence techniques
to the discipline of cryptanalysis.
Bandit-Based Random Mutation Hill-Climbing
Authors: Jialin Liu, Diego Peŕez-Liebana, Simon M. Lucas
Date: 2016-06-20 00:00:00

The Random Mutation Hill-Climbing algorithm is a direct search technique
mostly used in discrete domains. It repeats the process of randomly selecting a
neighbour of a best-so-far solution and accepts the neighbour if it is better
than or equal to it. In this work, we propose to use a novel method to select
the neighbour solution using a set of independent multi- armed bandit-style
selection units which results in a bandit-based Random Mutation Hill-Climbing
algorithm. The new algorithm significantly outperforms Random Mutation
Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road
problems (in the noise-free case). The algorithm shows particular promise for
discrete optimisation problems where each fitness evaluation is expensive.
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
Authors: Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker
Date: 2016-04-19 00:00:00
Proceeding: CVPR 2016 6

We present an approach to matching images of objects in fine-grained datasets
without using part annotations, with an application to the challenging problem
of weakly supervised single-view reconstruction. This is in contrast to prior
works that require part annotations, since matching objects across class and
pose variations is challenging with appearance features alone. We overcome this
challenge through a novel deep learning architecture, WarpNet, that aligns an
object in one image with a different object in another. We exploit the
structure of the fine-grained dataset to create artificial data for training
this network in an unsupervised-discriminative learning approach. The output of
the network acts as a spatial prior that allows generalization at test time to
match real images across variations in appearance, viewpoint and articulation.
On the CUB-200-2011 dataset of bird categories, we improve the AP over an
appearance-only network by 13.6%. We further demonstrate that our WarpNet
matches, together with the structure of fine-grained datasets, allow
single-view reconstructions with quality comparable to using annotated point
correspondences.
The LAMBADA dataset: Word prediction requiring a broad discourse context
Authors: Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, Raquel Fernández
Date: 2016-06-20 00:00:00
Proceeding: ACL 2016 8
Tasks: LAMBADA, Sentence

We introduce LAMBADA, a dataset to evaluate the capabilities of computational
models for text understanding by means of a word prediction task. LAMBADA is a
collection of narrative passages sharing the characteristic that human subjects
are able to guess their last word if they are exposed to the whole passage, but
not if they only see the last sentence preceding the target word. To succeed on
LAMBADA, computational models cannot simply rely on local context, but must be
able to keep track of information in the broader discourse. We show that
LAMBADA exemplifies a wide range of linguistic phenomena, and that none of
several state-of-the-art language models reaches accuracy above 1% on this
novel benchmark. We thus propose LAMBADA as a challenging test set, meant to
encourage the development of new models capable of genuine understanding of
broad context in natural language text.
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
Authors: Carsten Gottschlich, Benjamin Tams, Stephan Huckemann
Date: 2016-06-20 00:00:00
Tasks: Image Enhancement

Fingerprint recognition is widely used for verification and identification in
many commercial, governmental and forensic applications. The orientation field
(OF) plays an important role at various processing stages in fingerprint
recognition systems. OFs are used for image enhancement, fingerprint alignment,
for fingerprint liveness detection, fingerprint alteration detection and
fingerprint matching. In this paper, a novel approach is presented to globally
model an OF combined with locally adaptive methods. We show that this model
adapts perfectly to the 'true OF' in the limit. This perfect OF is described by
a small number of parameters with straightforward geometric interpretation.
Applications are manifold: Quick expert marking of very poor quality (for
instance latent) OFs, high fidelity low parameter OF compression and a direct
road to ground truth OFs markings for large databases, say. In this
contribution we describe an algorithm to perfectly estimate OF parameters
automatically or semi-automatically, depending on image quality, and we
establish the main underlying claim of high fidelity low parameter OF
compression.
The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingual Media Monitoring
Authors: Normunds Gruzitis, Guntis Barzdins
Date: 2016-06-20 00:00:00
Tasks: Abstractive Text Summarization, Position, Semantic Parsing

In the era of Big Data and Deep Learning, there is a common view that machine
learning approaches are the only way to cope with the robust and scalable
information extraction and summarization. It has been recently proposed that
the CNL approach could be scaled up, building on the concept of embedded CNL
and, thus, allowing for CNL-based information extraction from e.g. normative or
medical texts that are rather controlled by nature but still infringe the
boundaries of CNL. Although it is arguable if CNL can be exploited to approach
the robust wide-coverage semantic parsing for use cases like media monitoring,
its potential becomes much more obvious in the opposite direction: generation
of story highlights from the summarized AMR graphs, which is in the focus of
this position paper.
A New Training Method for Feedforward Neural Networks Based on Geometric Contraction Property of Activation Functions
Authors: Petre Birtea, Cosmin Cernazanu-Glavan, Alexandru Sisu
Date: 2016-06-20 00:00:00
Tasks: General Classification

We propose a new training method for a feedforward neural network having the
activation functions with the geometric contraction property. The method
consists of constructing a new functional that is less nonlinear in comparison
with the classical functional by removing the nonlinearity of the activation
function from the output layer. We validate this new method by a series of
experiments that show an improved learning speed and better classification
error.
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
Authors: Lin Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-01-27 00:00:00
Tasks: Person Re-Identification

In this paper, we propose a deep end-to-end neu- ral network to
simultaneously learn high-level features and a corresponding similarity metric
for person re-identification. The network takes a pair of raw RGB images as
input, and outputs a similarity value indicating whether the two input images
depict the same person. A layer of computing neighborhood range differences
across two input images is employed to capture local relationship between
patches. This operation is to seek a robust feature from input images. By
increasing the depth to 10 weight layers and using very small (3$\times$3)
convolution filters, our architecture achieves a remarkable improvement on the
prior-art configurations. Meanwhile, an adaptive Root- Mean-Square (RMSProp)
gradient decent algorithm is integrated into our architecture, which is
beneficial to deep nets. Our method consistently outperforms state-of-the-art
on two large datasets (CUHK03 and Market-1501), and a medium-sized data set
(CUHK01).
A New Parallel Algorithm for Two-Pass Connected Component Labeling
Authors: Siddharth Gupta, Diana Palsetia, Md. Mostofa Ali Patwary, Ankit Agrawal, Alok Choudhary
Date: 2016-06-20 00:00:00
Tasks: Vocal Bursts Valence Prediction

Connected Component Labeling (CCL) is an important step in pattern
recognition and image processing. It assigns labels to the pixels such that
adjacent pixels sharing the same features are assigned the same label.
Typically, CCL requires several passes over the data. We focus on two-pass
technique where each pixel is given a provisional label in the first pass
whereas an actual label is assigned in the second pass.
  We present a scalable parallel two-pass CCL algorithm, called PAREMSP, which
employs a scan strategy and the best union-find technique called REMSP, which
uses REM's algorithm for storing label equivalence information of pixels in a
2-D image. In the first pass, we divide the image among threads and each thread
runs the scan phase along with REMSP simultaneously. In the second phase, we
assign the final labels to the pixels. As REMSP is easily parallelizable, we
use the parallel version of REMSP for merging the pixels on the boundary. Our
experiments show the scalability of PAREMSP achieving speedups up to $20.1$
using $24$ cores on shared memory architecture using OpenMP for an image of
size $465.20$ MB. We find that our proposed parallel algorithm achieves linear
scaling for a large resolution fixed problem size as the number of processing
elements are increased. Additionally, the parallel algorithm does not make use
of any hardware specific routines, and thus is highly portable.
A Nonparametric Bayesian Approach for Spoken Term detection by Example Query
Authors: Amir Hossein Harati Nejad Torbati, Joseph Picone
Date: 2016-06-20 00:00:00
Tasks: Acoustic Unit Discovery, speech-recognition, Speech Recognition

State of the art speech recognition systems use data-intensive
context-dependent phonemes as acoustic units. However, these approaches do not
translate well to low resourced languages where large amounts of training data
is not available. For such languages, automatic discovery of acoustic units is
critical. In this paper, we demonstrate the application of nonparametric
Bayesian models to acoustic unit discovery. We show that the discovered units
are correlated with phonemes and therefore are linguistically meaningful. We
also present a spoken term detection (STD) by example query algorithm based on
these automatically learned units. We show that our proposed system produces a
P@N of 61.2% and an EER of 13.95% on the TIMIT dataset. The improvement in the
EER is 5% while P@N is only slightly lower than the best reported system in the
literature.
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method
Authors: Qiuyan Yan, Qifa Sun, Xinming Yan
Date: 2016-06-20 00:00:00
Tasks: Diversity, General Classification, Time Series, Time Series Analysis, Time Series Classification

ELM (Extreme Learning Machine) is a single hidden layer feed-forward network,
where the weights between input and hidden layer are initialized randomly. ELM
is efficient due to its utilization of the analytical approach to compute
weights between hidden and output layer. However, ELM still fails to output the
semantic classification outcome. To address such limitation, in this paper, we
propose a diversified top-k shapelets transform framework, where the shapelets
are the subsequences i.e., the best representative and interpretative features
of each class. As we identified, the most challenge problems are how to extract
the best k shapelets in original candidate sets and how to automatically
determine the k value. Specifically, we first define the similar shapelets and
diversified top-k shapelets to construct diversity shapelets graph. Then, a
novel diversity graph based top-k shapelets extraction algorithm named as
\textbf{DivTopkshapelets}\ is proposed to search top-k diversified shapelets.
Finally, we propose a shapelets transformed ELM algorithm named as
\textbf{DivShapELM} to automatically determine the k value, which is further
utilized for time series classification. The experimental results over public
data sets demonstrate that the proposed approach significantly outperforms
traditional ELM algorithm in terms of effectiveness and efficiency.
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
Authors: Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, Serge Belongie
Date: 2016-01-26 00:00:00
Tasks: Diversity, General Classification, Object Recognition, Optical Character Recognition, Optical Character Recognition (OCR), Scene Text Detection, Scene Understanding, Text Detection

This paper describes the COCO-Text dataset. In recent years large-scale
datasets like SUN and Imagenet drove the advancement of scene understanding and
object recognition. The goal of COCO-Text is to advance state-of-the-art in
text detection and recognition in natural images. The dataset is based on the
MS COCO dataset, which contains images of complex everyday scenes. The images
were not collected with text in mind and thus contain a broad variety of text
instances. To reflect the diversity of text in natural scenes, we annotate text
with (a) location in terms of a bounding box, (b) fine-grained classification
into machine printed text and handwritten text, (c) classification into legible
and illegible text, (d) script of the text and (e) transcriptions of legible
text. The dataset contains over 173k text annotations in over 63k images. We
provide a statistical analysis of the accuracy of our annotations. In addition,
we present an analysis of three leading state-of-the-art photo Optical
Character Recognition (OCR) approaches on our dataset. While scene text
detection and recognition enjoys strong advances in recent years, we identify
significant shortcomings motivating future work.
Minimum cost polygon overlay with rectangular shape stock panels
Authors: Wilson S. Siringoringo, Andy M. Connor, Nick Clements, Nick Alexander
Date: 2016-06-19 00:00:00

Minimum Cost Polygon Overlay (MCPO) is a unique two-dimensional optimization
problem that involves the task of covering a polygon shaped area with a series
of rectangular shaped panels. This has a number of applications in the
construction industry. This work examines the MCPO problem in order to
construct a model that captures essential parameters of the problem to be
solved automatically using numerical optimization algorithms. Three algorithms
have been implemented of the actual optimization task: the greedy search, the
Monte Carlo (MC) method, and the Genetic Algorithm (GA). Results are presented
to show the relative effectiveness of the algorithms. This is followed by
critical analysis of various findings of this research.
Graph based manifold regularized deep neural networks for automatic speech recognition
Authors: Vikrant Singh Tomar, Richard C. Rose
Date: 2016-06-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Deep neural networks (DNNs) have been successfully applied to a wide variety
of acoustic modeling tasks in recent years. These include the applications of
DNNs either in a discriminative feature extraction or in a hybrid acoustic
modeling scenario. Despite the rapid progress in this area, a number of
challenges remain in training DNNs. This paper presents an effective way of
training DNNs using a manifold learning based regularization framework. In this
framework, the parameters of the network are optimized to preserve underlying
manifold based relationships between speech feature vectors while minimizing a
measure of loss between network outputs and targets. This is achieved by
incorporating manifold based locality constraints in the objective criterion of
DNNs. Empirical evidence is provided to demonstrate that training a network
with manifold constraints preserves structural compactness in the hidden layers
of the network. Manifold regularization is applied to train bottleneck DNNs for
feature extraction in hidden Markov model (HMM) based speech recognition. The
experiments in this work are conducted on the Aurora-2 spoken digits and the
Aurora-4 read news large vocabulary continuous speech recognition tasks. The
performance is measured in terms of word error rate (WER) on these tasks. It is
shown that the manifold regularized DNNs result in up to 37% reduction in WER
relative to standard DNNs.