pwc-archive/papers-with-abstracts huggingface dataset:
Neural Person Search Machines
Authors: Hao Liu, Jiashi Feng, Zequn Jie, Karlekar Jayashree, Bo Zhao, Meibin Qi, Jianguo Jiang, Shuicheng Yan
Date: 2017-07-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Person Search

We investigate the problem of person search in the wild in this work. Instead
of comparing the query against all candidate regions generated in a query-blind
manner, we propose to recursively shrink the search area from the whole image
till achieving precise localization of the target person, by fully exploiting
information from the query and contextual cues in every recursive search step.
We develop the Neural Person Search Machines (NPSM) to implement such recursive
localization for person search. Benefiting from its neural search mechanism,
NPSM is able to selectively shrink its focus from a loose region to a tighter
one containing the target automatically. In this process, NPSM employs an
internal primitive memory component to memorize the query representation which
modulates the attention and augments its robustness to other distracting
regions. Evaluations on two benchmark datasets, CUHK-SYSU Person Search dataset
and PRW dataset, have demonstrated that our method can outperform current
state-of-the-arts in both mAP and top-1 evaluation protocols.
Improved Bilinear Pooling with CNNs
Authors: Tsung-Yu Lin, Subhransu Maji
Date: 2017-07-21 00:00:00
Tasks: GPU, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and
their compact variants [10], have been shown to be effective at fine-grained
recognition, scene categorization, texture recognition, and visual
question-answering tasks among others. The resulting representation captures
second-order statistics of convolutional features in a translationally
invariant manner. In this paper we investigate various ways of normalizing
these statistics to improve their representation power. In particular we find
that the matrix square-root normalization offers significant improvements and
outperforms alternative schemes such as the matrix logarithm normalization when
combined with elementwise square-root and l2 normalization. This improves the
accuracy by 2-3% on a range of fine-grained recognition datasets leading to a
new state of the art. We also investigate how the accuracy of matrix function
computations effect network training and evaluation. In particular we compare
against a technique for estimating matrix square-root gradients via solving a
Lyapunov equation that is more numerically accurate than computing gradients
via a Singular Value Decomposition (SVD). We find that while SVD gradients are
numerically inaccurate the overall effect on the final accuracy is negligible
once boundary cases are handled carefully. We present an alternative scheme for
computing gradients that is faster and yet it offers improvements over the
baseline model. Finally we show that the matrix square-root computed
approximately using a few Newton iterations is just as accurate for the
classification task but allows an order-of-magnitude faster GPU implementation
compared to SVD decomposition.
On the Consistency of Ordinal Regression Methods
Authors: Fabian Pedregosa, Francis Bach, Alexandre Gramfort
Date: 2014-08-11 00:00:00
Tasks: Binary Classification, General Classification, regression

Many of the ordinal regression models that have been proposed in the
literature can be seen as methods that minimize a convex surrogate of the
zero-one, absolute, or squared loss functions. A key property that allows to
study the statistical implications of such approximations is that of Fisher
consistency. Fisher consistency is a desirable property for surrogate loss
functions and implies that in the population setting, i.e., if the probability
distribution that generates the data were available, then optimization of the
surrogate would yield the best possible model. In this paper we will
characterize the Fisher consistency of a rich family of surrogate loss
functions used in the context of ordinal regression, including support vector
ordinal regression, ORBoosting and least absolute deviation. We will see that,
for a family of surrogate loss functions that subsumes support vector ordinal
regression and ORBoosting, consistency can be fully characterized by the
derivative of a real-valued function at zero, as happens for convex
margin-based surrogates in binary classification. We also derive excess risk
bounds for a surrogate of the absolute error that generalize existing risk
bounds for binary classification. Finally, our analysis suggests a novel
surrogate of the squared error loss. We compare this novel surrogate with
competing approaches on 9 different datasets. Our method shows to be highly
competitive in practice, outperforming the least squares loss on 7 out of 9
datasets.
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics
Authors: Kelum Gajamannage, Randy Paffenroth, Erik M. Bollt
Date: 2017-07-21 00:00:00
Tasks: Dimensionality Reduction

Existing dimensionality reduction methods are adept at revealing hidden
underlying manifolds arising from high-dimensional data and thereby producing a
low-dimensional representation. However, the smoothness of the manifolds
produced by classic techniques over sparse and noisy data is not guaranteed. In
fact, the embedding generated using such data may distort the geometry of the
manifold and thereby produce an unfaithful embedding. Herein, we propose a
framework for nonlinear dimensionality reduction that generates a manifold in
terms of smooth geodesics that is designed to treat problems in which manifold
measurements are either sparse or corrupted by noise. Our method generates a
network structure for given high-dimensional data using a nearest neighbors
search and then produces piecewise linear shortest paths that are defined as
geodesics. Then, we fit points in each geodesic by a smoothing spline to
emphasize the smoothness. The robustness of this approach for sparse and noisy
datasets is demonstrated by the implementation of the method on synthetic and
real-world datasets.
An Infinite Hidden Markov Model With Similarity-Biased Transitions
Authors: Colin Reimer Dawson, Chaofan Huang, Clayton T. Morrison
Date: 2017-07-21 00:00:00
Proceeding: ICML 2017 8
Tasks: speaker-diarization, Speaker Diarization

We describe a generalization of the Hierarchical Dirichlet Process Hidden
Markov Model (HDP-HMM) which is able to encode prior information that state
transitions are more likely between "nearby" states. This is accomplished by
defining a similarity function on the state space and scaling transition
probabilities by pair-wise similarities, thereby inducing correlations among
the transition distributions. We present an augmented data representation of
the model as a Markov Jump Process in which: (1) some jump attempts fail, and
(2) the probability of success is proportional to the similarity between the
source and destination states. This augmentation restores conditional conjugacy
and admits a simple Gibbs sampler. We evaluate the model and inference method
on a speaker diarization task and a "harmonic parsing" task using four-part
chorale data, as well as on several synthetic datasets, achieving favorable
comparisons to existing models.
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Authors: Lachlan Tychsen-Smith, Lars Petersson
Date: 2017-03-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: object-detection, Object Detection, Object Localization, Real-Time Object Detection

We define the object detection from imagery problem as estimating a very
large but extremely sparse bounding box dependent probability distribution.
Subsequently we identify a sparse distribution estimation scheme, Directed
Sparse Sampling, and employ it in a single end-to-end CNN based detection
model. This methodology extends and formalizes previous state-of-the-art
detection models with an additional emphasis on high evaluation rates and
reduced manual engineering. We introduce two novelties, a corner based
region-of-interest estimator and a deconvolution based CNN model. The resulting
model is scene adaptive, does not require manually defined reference bounding
boxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and
Pascal VOC 2012 with real-time evaluation rates. Further analysis suggests our
model performs particularly well when finegrained object localization is
desirable. We argue that this advantage stems from the significantly larger set
of available regions-of-interest relative to other methods. Source-code is
available from: https://github.com/lachlants/denet
Predictive networking and optimization for flow-based networks
Authors: Michael Arnold
Date: 2017-07-21 00:00:00

Artificial Neural Networks (ANNs) were used to classify neural network flows
by flow size. After training the neural network was able to predict the size of
a flows with 87% accuracy with a Feed Forward Neural Network. This demonstrates
that flow based routers can prioritize candidate flows with a predicted large
number of packets for priority insertion into hardware content-addressable
memory.
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level
Authors: Thai-Hoang Pham, Phuong Le-Hong
Date: 2017-05-11 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

This paper demonstrates end-to-end neural network architectures for
Vietnamese named entity recognition. Our best model is a combination of
bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves an F1 score of 88.59% on a standard test set. Our system
is able to achieve a comparable performance to the first-rank system of the
VLSP campaign without using any syntactic or hand-crafted features. We also
give an extensive empirical study on using common deep learning models for
Vietnamese NER, at both word and character level.
Generalized Convolutional Neural Networks for Point Cloud Data
Authors: Aleksandr Savchenkov, Andrew Davis, Xuan Zhao
Date: 2017-07-20 00:00:00
Tasks: Feature Engineering

The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices
has given the computer vision community 3D information that conventional RGB
cameras cannot provide. This data is often stored as a point cloud. In this
paper, we present a novel method to apply the concept of convolutional neural
networks to this type of data. By creating a mapping of nearest neighbors in a
dataset, and individually applying weights to spatial relationships between
points, we achieve an architecture that works directly with point clouds, but
closely resembles a convolutional neural net in both design and behavior. Such
a method bypasses the need for extensive feature engineering, while proving to
be computationally efficient and requiring few parameters.
Convolutional Sparse Coding: Boundary Handling Revisited
Authors: Brendt Wohlberg, Paul Rodriguez
Date: 2017-07-20 00:00:00
Tasks: Deblurring, Image Deblurring

Two different approaches have recently been proposed for boundary handling in
convolutional sparse representations, avoiding potential boundary artifacts
arising from the circular boundary conditions implied by the use of frequency
domain solution methods by introducing a spatial mask into the convolutional
sparse coding problem. In the present paper we show that, under certain
circumstances, these methods fail in their design goal of avoiding boundary
artifacts. The reasons for this failure are discussed, a solution is proposed,
and the practical implications are illustrated in an image deblurring problem.
On kernel methods for covariates that are rankings
Authors: Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael. I. Jordan, Benjamin Recht
Date: 2016-03-25 00:00:00
Tasks: regression

Permutation-valued features arise in a variety of applications, either in a
direct way when preferences are elicited over a collection of items, or an
indirect way in which numerical ratings are converted to a ranking. To date,
there has been relatively limited study of regression, classification, and
testing problems based on permutation-valued features, as opposed to
permutation-valued responses. This paper studies the use of reproducing kernel
Hilbert space methods for learning from permutation-valued features. These
methods embed the rankings into an implicitly defined function space, and allow
for efficient estimation of regression and test functions in this richer space.
Our first contribution is to characterize both the feature spaces and spectral
properties associated with two kernels for rankings, the Kendall and Mallows
kernels. Using tools from representation theory, we explain the limited
expressive power of the Kendall kernel by characterizing its degenerate
spectrum, and in sharp contrast, we prove that Mallows' kernel is universal and
characteristic. We also introduce families of polynomial kernels that
interpolate between the Kendall (degree one) and Mallows' (infinite degree)
kernels. We show the practical effectiveness of our methods via applications to
Eurobarometer survey data as well as a Movielens ratings dataset.
Overcoming Catastrophic Interference by Conceptors
Authors: Xu He, Herbert Jaeger
Date: 2017-07-16 00:00:00
Tasks: Continual Learning

Catastrophic interference has been a major roadblock in the research of
continual learning. Here we propose a variant of the back-propagation
algorithm, "conceptor-aided back-prop" (CAB), in which gradients are shielded
by conceptors against degradation of previously learned tasks. Conceptors have
their origin in reservoir computing, where they have been previously shown to
overcome catastrophic forgetting. CAB extends these results to deep feedforward
networks. On the disjoint MNIST task CAB outperforms two other methods for
coping with catastrophic interference that have recently been proposed in the
deep learning field.
Learning variable length units for SMT between related languages via Byte Pair Encoding
Authors: Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2016-10-20 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

We explore the use of segments learnt using Byte Pair Encoding (referred to
as BPE units) as basic units for statistical machine translation between
related languages and compare it with orthographic syllables, which are
currently the best performing basic units for this translation task. BPE
identifies the most frequent character sequences as basic units, while
orthographic syllables are linguistically motivated pseudo-syllables. We show
that BPE units modestly outperform orthographic syllables as units of
translation, showing up to 11% increase in BLEU score. While orthographic
syllables can be used only for languages whose writing systems use vowel
representations, BPE is writing system independent and we show that BPE
outperforms other units for non-vowel writing systems too. Our results are
supported by extensive experimentation spanning multiple language families and
writing systems.
Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models
Authors: Harsh Jhamtani, Varun Gangal, Eduard Hovy, Eric Nyberg
Date: 2017-07-04 00:00:00

Variations in writing styles are commonly used to adapt the content to a
specific context, audience, or purpose. However, applying stylistic variations
is still by and large a manual process, and there have been little efforts
towards automating it. In this paper we explore automated methods to transform
text from modern English to Shakespearean English using an end to end trainable
neural model with pointers to enable copy action. To tackle limited amount of
parallel data, we pre-train embeddings of words by leveraging external
dictionaries mapping Shakespearean words to modern English words as well as
additional text. Our methods are able to get a BLEU score of 31+, an
improvement of ~6 points above the strongest baseline. We publicly release our
code to foster further research in this area.
Local Geometry Inclusive Global Shape Representation
Authors: Somenath Das, Suchendra M. Bhandarkar
Date: 2017-07-20 00:00:00

Knowledge of shape geometry plays a pivotal role in many shape analysis
applications. In this paper we introduce a local geometry-inclusive global
representation of 3D shapes based on computation of the shortest quasi-geodesic
paths between all possible pairs of points on the 3D shape manifold. In the
proposed representation, the normal curvature along the quasi-geodesic paths
between any two points on the shape surface is preserved. We employ the
eigenspectrum of the proposed global representation to address the problems of
determination of region-based correspondence between isometric shapes and
characterization of self-symmetry in the absence of prior knowledge in the form
of user-defined correspondence maps. We further utilize the commutative
property of the resulting shape descriptor to extract stable regions between
isometric shapes that differ from one another by a high degree of isometry
transformation. We also propose various shape characterization metrics in terms
of the eigenvector decomposition of the shape descriptor spectrum to quantify
the correspondence and self-symmetry of 3D shapes. The performance of the
proposed 3D shape descriptor is experimentally compared with the performance of
other relevant state-of-the-art 3D shape descriptors.
Conditional Image Synthesis With Auxiliary Classifier GANs
Authors: Augustus Odena, Christopher Olah, Jonathon Shlens
Date: 2016-10-30 00:00:00
Proceeding: ICML 2017 8
Tasks: Conditional Image Generation, Diversity, Image Generation, Image Quality Assessment

Synthesizing high resolution photorealistic images has been a long-standing
challenge in machine learning. In this paper we introduce new methods for the
improved training of generative adversarial networks (GANs) for image
synthesis. We construct a variant of GANs employing label conditioning that
results in 128x128 resolution image samples exhibiting global coherence. We
expand on previous work for image quality assessment to provide two new
analyses for assessing the discriminability and diversity of samples from
class-conditional image synthesis models. These analyses demonstrate that high
resolution samples provide class information not present in low resolution
samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as
discriminable as artificially resized 32x32 samples. In addition, 84.7% of the
classes have samples exhibiting diversity comparable to real ImageNet data.
Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture
Authors: Regina Meszlényi, Krisztian Buza, Zoltán Vidnyánszky
Date: 2017-07-20 00:00:00
Tasks: Classification, Functional Connectivity, General Classification

Machine learning techniques have become increasingly popular in the field of
resting state fMRI (functional magnetic resonance imaging) network based
classification. However, the application of convolutional networks has been
proposed only very recently and has remained largely unexplored. In this paper
we describe a convolutional neural network architecture for functional
connectome classification called connectome-convolutional neural network
(CCNN). Our results on simulated datasets and a publicly available dataset for
amnestic mild cognitive impairment classification demonstrate that our CCNN
model can efficiently distinguish between subject groups. We also show that the
connectome-convolutional network is capable to combine information from diverse
functional connectivity metrics and that models using a combination of
different connectivity descriptors are able to outperform classifiers using
only one metric. From this flexibility follows that our proposed CCNN model can
be easily adapted to a wide range of connectome based classification or
regression tasks, by varying which connectivity descriptor combinations are
used to train the network.
Prediction of Dynamical time Series Using Kernel Based Regression and Smooth Splines
Authors: Raymundo Navarrete, Divakar Viswanath
Date: 2015-10-31 00:00:00
Tasks: Denoising, regression, Time Series, Time Series Analysis

Prediction of dynamical time series with additive noise using support vector
machines or kernel based regression has been proved to be consistent for
certain classes of discrete dynamical systems. Consistency implies that these
methods are effective at computing the expected value of a point at a future
time given the present coordinates. However, the present coordinates themselves
are noisy, and therefore, these methods are not necessarily effective at
removing noise. In this article, we consider denoising and prediction as
separate problems for flows, as opposed to discrete time dynamical systems, and
show that the use of smooth splines is more effective at removing noise.
Combination of smooth splines and kernel based regression yields predictors
that are more accurate on benchmarks typically by a factor of 2 or more. We
prove that kernel based regression in combination with smooth splines converges
to the exact predictor for time series extracted from any compact invariant set
of any sufficiently smooth flow. As a consequence of convergence, one can find
examples where the combination of kernel based regression with smooth splines
is superior by even a factor of $100$. The predictors that we compute operate
on delay coordinate data and not the full state vector, which is typically not
observable.
Distance-based species tree estimation: information-theoretic trade-off between number of loci and sequence length under the coalescent
Authors: Elchanan Mossel, Sebastien Roch
Date: 2015-04-21 00:00:00

We consider the reconstruction of a phylogeny from multiple genes under the
multispecies coalescent. We establish a connection with the sparse signal
detection problem, where one seeks to distinguish between a distribution and a
mixture of the distribution and a sparse signal. Using this connection, we
derive an information-theoretic trade-off between the number of genes, $m$,
needed for an accurate reconstruction and the sequence length, $k$, of the
genes. Specifically, we show that to detect a branch of length $f$, one needs
$m = \Theta(1/[f^{2} \sqrt{k}])$.
Captioning Images with Diverse Objects
Authors: Subhashini Venugopalan, Lisa Anne Hendricks, Marcus Rohrbach, Raymond Mooney, Trevor Darrell, Kate Saenko
Date: 2016-06-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Recognition

Recent captioning models are limited in their ability to scale and describe
concepts unseen in paired image-text corpora. We propose the Novel Object
Captioner (NOC), a deep visual semantic captioning model that can describe a
large number of object categories not present in existing image-caption
datasets. Our model takes advantage of external sources -- labeled images from
object recognition datasets, and semantic knowledge extracted from unannotated
text. We propose minimizing a joint objective which can learn from these
diverse data sources and leverage distributional semantic embeddings, enabling
the model to generalize and describe novel objects outside of image-caption
datasets. We demonstrate that our model exploits semantic information to
generate captions for hundreds of object categories in the ImageNet object
recognition dataset that are not observed in MSCOCO image-caption training
data, as well as many categories that are observed very rarely. Both automatic
evaluations and human judgements show that our model considerably outperforms
prior work in being able to describe many more categories of objects.