pwc-archive/papers-with-abstracts huggingface dataset:
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
Authors: Anza Shakeel, Mohsen Ali
Date: 2017-05-12 00:00:00
Tasks: Object Recognition

Deep convolutional neural networks (CNNs) have outperformed existing object
recognition and detection algorithms. On the other hand satellite imagery
captures scenes that are diverse. This paper describes a deep learning approach
that analyzes a geo referenced satellite image and efficiently detects built
structures in it. A Fully Convolution Network (FCN) is trained on low
resolution Google earth satellite imagery in order to achieve end result. The
detected built communities are then correlated with the vaccination activity
that has furnished some useful statistics.
Joint Semantic Synthesis and Morphological Analysis of the Derived Word
Authors: Ryan Cotterell, Hinrich Schütze
Date: 2017-01-04 00:00:00
Proceeding: TACL 2018 1
Tasks: Additive models, Morphological Analysis

Much like sentences are composed of words, words themselves are composed of
smaller units. For example, the English word questionably can be analyzed as
question+able+ly. However, this structural decomposition of the word does not
directly give us a semantic representation of the word's meaning. Since
morphology obeys the principle of compositionality, the semantics of the word
can be systematically derived from the meaning of its parts. In this work, we
propose a novel probabilistic model of word formation that captures both the
analysis of a word w into its constituents segments and the synthesis of the
meaning of w from the meanings of those segments. Our model jointly learns to
segment words into morphemes and compose distributional semantic vectors of
those morphemes. We experiment with the model on English CELEX data and German
DerivBase (Zeller et al., 2013) data. We show that jointly modeling semantics
increases both segmentation accuracy and morpheme F1 by between 3% and 5%.
Additionally, we investigate different models of vector composition, showing
that recurrent neural networks yield an improvement over simple additive
models. Finally, we study the degree to which the representations correspond to
a linguist's notion of morphological productivity.
Adaptive Feature Representation for Visual Tracking
Authors: Yuqi Han, Chenwei Deng, Zengshuo Zhang, Jiatong Li, Baojun Zhao
Date: 2017-05-12 00:00:00
Tasks: Visual Tracking

Robust feature representation plays significant role in visual tracking.
However, it remains a challenging issue, since many factors may affect the
experimental performance. The existing method which combine different features
by setting them equally with the fixed weight could hardly solve the issues,
due to the different statistical properties of different features across
various of scenarios and attributes. In this paper, by exploiting the internal
relationship among these features, we develop a robust method to construct a
more stable feature representation. More specifically, we utilize a co-training
paradigm to formulate the intrinsic complementary information of multi-feature
template into the efficient correlation filter framework. We test our approach
on challenging se- quences with illumination variation, scale variation,
deformation etc. Experimental results demonstrate that the proposed method
outperforms state-of-the-art methods favorably.
Arc-swift: A Novel Transition System for Dependency Parsing
Authors: Peng Qi, Christopher D. Manning
Date: 2017-05-12 00:00:00
Proceeding: ACL 2017 7
Tasks: ARC, Dependency Parsing, Sentence

Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enables direct attachments between tokens farther apart with a single
transition. This allows the parser to leverage lexical information more
directly in transition decisions. Hence, arc-swift can achieve significantly
better performance with a very small beam size. Our parsers reduce error by
3.7--7.6% relative to those using existing transition systems on the Penn
Treebank dependency parsing task and English Universal Dependencies.
View-Invariant Template Matching Using Homography Constraints
Authors: Sina Lotfian, Hassan Foroosh
Date: 2017-05-12 00:00:00
Tasks: Object, Object Recognition, Template Matching

Change in viewpoint is one of the major factors for variation in object
appearance across different images. Thus, view-invariant object recognition is
a challenging and important image understanding task. In this paper, we propose
a method that can match objects in images taken under different viewpoints.
Unlike most methods in the literature, no restriction on camera orientations or
internal camera parameters are imposed and no prior knowledge of 3D structure
of the object is required. We prove that when two cameras take pictures of the
same object from two different viewing angels, the relationship between every
quadruple of points reduces to the special case of homography with two equal
eigenvalues. Based on this property, we formulate the problem as an error
function that indicates how likely two sets of 2D points are projections of the
same set of 3D points under two different cameras. Comprehensive set of
experiments were conducted to prove the robustness of the method to noise, and
evaluate its performance on real-world applications, such as face and object
recognition.
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Authors: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
Date: 2016-06-02 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.
Predicting popularity of online videos using Support Vector Regression
Authors: Tomasz Trzcinski, Przemyslaw Rokita
Date: 2015-10-21 00:00:00
Tasks: regression

In this work, we propose a regression method to predict the popularity of an
online video based on temporal and visual cues. Our method uses Support Vector
Regression with Gaussian Radial Basis Functions. We show that modelling
popularity patterns with this approach provides higher and more stable
prediction results, mainly thanks to the non-linearity character of the
proposed method as well as its resistance against overfitting. We compare our
method with the state of the art on datasets containing over 14,000 videos from
YouTube and Facebook. Furthermore, we show that results obtained relying only
on the early distribution patterns, can be improved by adding social and visual
metadata.
Modeling of the Latent Embedding of Music using Deep Neural Network
Authors: Zhou Xing, Eddy Baik, Yan Jiao, Nilesh Kulkarni, Chris Li, Gautam Muralidhar, Marzieh Parandehgheibi, Erik Reed, Abhishek Singhal, Fei Xiao, Chris Pouliot
Date: 2017-05-12 00:00:00
Tasks: Collaborative Filtering

While both the data volume and heterogeneity of the digital music content is
huge, it has become increasingly important and convenient to build a
recommendation or search system to facilitate surfacing these content to the
user or consumer community. Most of the recommendation models fall into two
primary species, collaborative filtering based and content based approaches.
Variants of instantiations of collaborative filtering approach suffer from the
common issues of so called "cold start" and "long tail" problems where there is
not much user interaction data to reveal user opinions or affinities on the
content and also the distortion towards the popular content. Content-based
approaches are sometimes limited by the richness of the available content data
resulting in a heavily biased and coarse recommendation result. In recent
years, the deep neural network has enjoyed a great success in large-scale image
and video recognitions. In this paper, we propose and experiment using deep
convolutional neural network to imitate how human brain processes hierarchical
structures in the auditory signals, such as music, speech, etc., at various
timescales. This approach can be used to discover the latent factor models of
the music based upon acoustic hyper-images that are extracted from the raw
audio waves of music. These latent embeddings can be used either as features to
feed to subsequent models, such as collaborative filtering, or to build
similarity metrics between songs, or to classify music based on the labels for
training such as genre, mood, sentiment, etc.
Reducing Bias in Production Speech Models
Authors: Eric Battenberg, Rewon Child, Adam Coates, Christopher Fougner, Yashesh Gaur, Jiaji Huang, Heewoo Jun, Ajay Kannan, Markus Kliegl, Atul Kumar, Hairong Liu, Vinay Rao, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu
Date: 2017-05-11 00:00:00
Tasks: Object Recognition

Replacing hand-engineered pipelines with end-to-end deep learning systems has
enabled strong results in applications like speech and object recognition.
However, the causality and latency constraints of production systems put
end-to-end speech models back into the underfitting regime and expose biases in
the model that we show cannot be overcome by "scaling up", i.e., training
bigger models on more data. In this work we systematically identify and address
sources of bias, reducing error rates by up to 20% while remaining practical
for deployment. We achieve this by utilizing improved neural architectures for
streaming inference, solving optimization issues, and employing strategies that
increase audio and label modelling versatility.
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Authors: Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, Robert Jenssen
Date: 2017-05-11 00:00:00
Tasks: Load Forecasting, Time Series, Time Series Analysis

The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to the design and development of methodologies for short term
load forecasting over the past decades. A class of mathematical models, called
Recurrent Neural Networks, are nowadays gaining renewed interest among
researchers and they are replacing many practical implementation of the
forecasting systems, previously based on static methods. Despite the undeniable
expressive power of these architectures, their recurrent nature complicates
their understanding and poses challenges in the training procedures. Recently,
new important families of recurrent architectures have emerged and their
applicability in the context of load forecasting has not been investigated
completely yet. In this paper we perform a comparative study on the problem of
Short-Term Load Forecast, by using different classes of state-of-the-art
Recurrent Neural Networks. We test the reviewed models first on controlled
synthetic tasks and then on different real datasets, covering important
practical cases of study. We provide a general overview of the most important
architectures and we define guidelines for configuring the recurrent networks
to predict real-valued time series.
Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
Authors: Jiwei Li, Alan Ritter, Dan Jurafsky
Date: 2015-10-18 00:00:00
Tasks: Community Detection, Link Prediction

Inferring latent attributes of people online is an important social computing
task, but requires integrating the many heterogeneous sources of information
available on the web. We propose learning individual representations of people
using neural nets to integrate rich linguistic and network evidence gathered
from social media. The algorithm is able to combine diverse cues, such as the
text a person writes, their attributes (e.g. gender, employer, education,
location) and social relations to other people. We show that by integrating
both textual and network evidence, these representations offer improved
performance at four important tasks in social media inference on Twitter:
predicting (1) gender, (2) occupation, (3) location, and (4) friendships for
users. Our approach scales to large datasets and the learned representations
can be used as general features in and have the potential to benefit a large
number of downstream tasks including link prediction, community detection, or
probabilistic reasoning over social networks.
Multigrid Neural Architectures
Authors: Tsung-Wei Ke, Michael Maire, Stella X. Yu
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Classification, Semantic Segmentation

We propose a multigrid extension of convolutional neural networks (CNNs).
Rather than manipulating representations living on a single spatial grid, our
network layers operate across scale space, on a pyramid of grids. They consume
multigrid inputs and produce multigrid outputs; convolutional filters
themselves have both within-scale and cross-scale extent. This aspect is
distinct from simple multiscale designs, which only process the input at
different scales. Viewed in terms of information flow, a multigrid network
passes messages across a spatial pyramid. As a consequence, receptive field
size grows exponentially with depth, facilitating rapid integration of context.
Most critically, multigrid structure enables networks to learn internal
attention and dynamic routing mechanisms, and use them to accomplish tasks on
which modern CNNs fail.
  Experiments demonstrate wide-ranging performance advantages of multigrid. On
CIFAR and ImageNet classification tasks, flipping from a single grid to
multigrid within the standard CNN paradigm improves accuracy, while being
compute and parameter efficient. Multigrid is independent of other
architectural choices; we show synergy in combination with residual
connections. Multigrid yields dramatic improvement on a synthetic semantic
segmentation dataset. Most strikingly, relatively shallow multigrid networks
can learn to directly perform spatial transformation tasks, where, in contrast,
current CNNs fail. Together, our results suggest that continuous evolution of
features on a multigrid pyramid is a more powerful alternative to existing CNN
designs on a flat grid.
A rational analysis of curiosity
Authors: Rachit Dubey, Thomas L. Griffiths
Date: 2017-05-11 00:00:00

We present a rational analysis of curiosity, proposing that people's curiosity is driven by seeking stimuli that maximize their ability to make appropriate responses in the future. This perspective offers a way to unify previous theories of curiosity into a single framework. Experimental results confirm our model's predictions, showing how the relationship between curiosity and confidence can change significantly depending on the nature of the environment. Please refer to https://psyarxiv.com/wg5m6/ for a more updated version of this manuscript with a more detailed modeling section with extensive experiments.
A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets
Authors: Thangarajah Akilan, Q. M. Jonathan Wu, Wei Jiang
Date: 2017-05-11 00:00:00
Tasks: Action Classification, Classification, General Classification, Image Categorization, image-classification, Image Classification

Following the rapidly growing digital image usage, automatic image
categorization has become preeminent research area. It has broaden and adopted
many algorithms from time to time, whereby multi-feature (generally,
hand-engineered features) based image characterization comes handy to improve
accuracy. Recently, in machine learning, pre-trained deep convolutional neural
networks (DCNNs or ConvNets) have been that the features extracted through such
DCNN can improve classification accuracy. Thence, in this paper, we further
investigate a feature embedding strategy to exploit cues from multiple DCNNs.
We derive a generalized feature space by embedding three different DCNN
bottleneck features with weights respect to their Softmax cross-entropy loss.
Test outcomes on six different object classification data-sets and an action
classification data-set show that regardless of variation in image statistics
and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to
image classification tasks and an effective complement of DCNN. The comparisons
to existing fusion-based image classification approaches prove that the
proposed method surmounts the state-of-the-art methods and produces competitive
results with fully trained DCNNs as well.
Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks
Authors: Hokchhay Tann, Soheil Hashemi, Iris Bahar, Sherief Reda
Date: 2017-05-11 00:00:00
Tasks: General Classification

While Deep Neural Networks (DNNs) push the state-of-the-art in many machine
learning applications, they often require millions of expensive floating-point
operations for each input classification. This computation overhead limits the
applicability of DNNs to low-power, embedded platforms and incurs high cost in
data centers. This motivates recent interests in designing low-power,
low-latency DNNs based on fixed-point, ternary, or even binary data precision.
While recent works in this area offer promising results, they often lead to
large accuracy drops when compared to the floating-point networks. We propose a
novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point
networks with integer power-of-two weights with no change in network
architecture. Our dynamic fixed-point DNNs allow different radix points between
layers. During inference, power-of-two weights allow multiplications to be
replaced with arithmetic shifts, while the 8-bit fixed-point representation
simplifies both the buffer and adder design. In addition, we propose a hardware
accelerator design to achieve low-power, low-latency inference with
insignificant degradation in accuracy. Using our custom accelerator design with
the CIFAR-10 and ImageNet datasets, we show that our method achieves
significant power and energy savings while increasing the classification
accuracy.
Controlling Perceptual Factors in Neural Style Transfer
Authors: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Manipulation, Style Transfer

Neural Style Transfer has shown very exciting results enabling new forms of
image manipulation. Here we extend the existing method to introduce control
over spatial location, colour information and across spatial scale. We
demonstrate how this enhances the method by allowing high-resolution controlled
stylisation and helps to alleviate common failure cases such as applying ground
textures to sky regions. Furthermore, by decomposing style into these
perceptual factors we enable the combination of style information from multiple
sources to generate new, perceptually appealing styles from existing ones. We
also describe how these methods can be used to more efficiently produce large
size, high-quality stylisation. Finally we show how the introduced control
measures can be applied in recent methods for Fast Neural Style Transfer.
Probabilistic Image Colorization
Authors: Amelie Royer, Alexander Kolesnikov, Christoph H. Lampert
Date: 2017-05-11 00:00:00
Tasks: Colorization, Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images.
In light of the intrinsic uncertainty of this task, the proposed probabilistic
framework has numerous desirable properties. In particular, our model is able
to produce multiple plausible and vivid colorizations for a given grayscale
image and is one of the first colorization models to provide a proper
stochastic sampling scheme. Moreover, our training procedure is supported by a
rigorous theoretical framework that does not require any ad hoc heuristics and
allows for efficient modeling and learning of the joint pixel color
distribution. We demonstrate strong quantitative and qualitative experimental
results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset.
Sketching Word Vectors Through Hashing
Authors: Behrang QasemiZadeh, Laura Kallmeyer
Date: 2017-05-11 00:00:00

We propose a new fast word embedding technique using hash functions. The
method is a derandomization of a new type of random projections: By
disregarding the classic constraint used in designing random projections (i.e.,
preserving pairwise distances in a particular normed space), our solution
exploits extremely sparse non-negative random projections. Our experiments show
that the proposed method can achieve competitive results, comparable to neural
embedding learning techniques, however, with only a fraction of the
computational complexity of these methods. While the proposed derandomization
enhances the computational and space complexity of our method, the possibility
of applying weighting methods such as positive pointwise mutual information
(PPMI) to our models after their construction (and at a reduced dimensionality)
imparts a high discriminatory power to the resulting embeddings. Obviously,
this method comes with other known benefits of random projection-based
techniques such as ease of update.