pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Stage Temporal Difference Learning for 2048-like Games
Authors: Kun-Hao Yeh, I-Chen Wu, Chu-Hsuan Hsueh, Chia-Chuan Chang, Chao-Chin Liang, Han Chiang
Date: 2016-06-23 00:00:00
Tasks: Hierarchical Reinforcement Learning, Playing the Game of 2048, Reinforcement Learning

Szubert and Jaskowski successfully used temporal difference (TD) learning
together with n-tuple networks for playing the game 2048. However, we observed
a phenomenon that the programs based on TD learning still hardly reach large
tiles. In this paper, we propose multi-stage TD (MS-TD) learning, a kind of
hierarchical reinforcement learning method, to effectively improve the
performance for the rates of reaching large tiles, which are good metrics to
analyze the strength of 2048 programs. Our experiments showed significant
improvements over the one without using MS-TD learning. Namely, using 3-ply
expectimax search, the program with MS-TD learning reached 32768-tiles with a
rate of 18.31%, while the one with TD learning did not reach any. After further
tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000
games, and one among these games even reached a 65536-tile, which is the first
ever reaching a 65536-tile to our knowledge. In addition, MS-TD learning method
can be easily applied to other 2048-like games, such as Threes. Based on MS-TD
learning, our experiments for Threes also demonstrated similar performance
improvement, where the program with MS-TD learning reached 6144-tiles with a
rate of 7.83%, while the one with TD learning only reached 0.45%.
Trainable Frontend For Robust and Far-Field Keyword Spotting
Authors: Yuxuan Wang, Pascal Getreuer, Thad Hughes, Richard F. Lyon, Rif A. Saurous
Date: 2016-07-19 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

Robust and far-field speech recognition is critical to enable true hands-free
communication. In far-field conditions, signals are attenuated due to distance.
To improve robustness to loudness variation, we introduce a novel frontend
called per-channel energy normalization (PCEN). The key ingredient of PCEN is
the use of an automatic gain control based dynamic compression to replace the
widely used static (such as log or root) compression. We evaluate PCEN on the
keyword spotting task. On our large rerecorded noisy and far-field eval sets,
we show that PCEN significantly improves recognition performance. Furthermore,
we model PCEN as neural network layers and optimize high-dimensional PCEN
parameters jointly with the keyword spotting acoustic model. The trained PCEN
frontend demonstrates significant further improvements without increasing model
complexity or inference-time cost.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Authors: Yan Xu, Yang Li, Mingyuan Liu, Yipei Wang, Yubo Fan, Maode Lai, Eric I-Chao Chang
Date: 2016-07-17 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a new image instance segmentation method that
segments individual glands (instances) in colon histology images. This is a
task called instance segmentation that has recently become increasingly
important. The problem is challenging since not only do the glands need to be
segmented from the complex background, they are also required to be
individually identified. Here we leverage the idea of image-to-image prediction
in recent deep learning by building a framework that automatically exploits and
fuses complex multichannel information, regional, location and boundary
patterns in gland histology images. Our proposed system, deep multichannel
framework, alleviates heavy feature design due to the use of convolutional
neural networks and is able to meet multifarious requirement by altering
channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation
Challenge and other currently prevalent methods of instance segmentation, we
observe state-of-the-art results based on a number of evaluation metrics.
On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra
Authors: C. Dafonte, D. Fustes, M. Manteiga, D. Garabato, M. A. Alvarez, A. Ulla, C. Allende Prieto
Date: 2016-07-19 00:00:00

Aims. We present an innovative artificial neural network (ANN) architecture,
called Generative ANN (GANN), that computes the forward model, that is it
learns the function that relates the unknown outputs (stellar atmospheric
parameters, in this case) to the given inputs (spectra). Such a model can be
integrated in a Bayesian framework to estimate the posterior distribution of
the outputs. Methods. The architecture of the GANN follows the same scheme as a
normal ANN, but with the inputs and outputs inverted. We train the network with
the set of atmospheric parameters (Teff, logg, [Fe/H] and [alpha/Fe]),
obtaining the stellar spectra for such inputs. The residuals between the
spectra in the grid and the estimated spectra are minimized using a validation
dataset to keep solutions as general as possible. Results. The performance of
both conventional ANNs and GANNs to estimate the stellar parameters as a
function of the star brightness is presented and compared for different
Galactic populations. GANNs provide significantly improved parameterizations
for early and intermediate spectral types with rich and intermediate
metallicities. The behaviour of both algorithms is very similar for our sample
of late-type stars, obtaining residuals in the derivation of [Fe/H] and
[alpha/Fe] below 0.1dex for stars with Gaia magnitude Grvs<12, which accounts
for a number in the order of four million stars to be observed by the Radial
Velocity Spectrograph of the Gaia satellite. Conclusions. Uncertainty
estimation of computed astrophysical parameters is crucial for the validation
of the parameterization itself and for the subsequent exploitation by the
astronomical community. GANNs produce not only the parameters for a given
spectrum, but a goodness-of-fit between the observed spectrum and the predicted
one for a given set of parameters. Moreover, they allow us to obtain the full
posterior distribution...
A Local-Global Approach to Semantic Segmentation in Aerial Images
Authors: Alina Elena Marcu
Date: 2016-07-19 00:00:00
Tasks: Semantic Segmentation

Aerial images are often taken under poor lighting conditions and contain low
resolution objects, many times occluded by other objects. In this domain,
visual context could be of great help, but there are still very few papers that
consider context in aerial image understanding and still remains an open
problem in computer vision. We propose a dual-stream deep neural network that
processes information along two independent pathways. Our model learns to
combine local and global appearance in a complementary way, such that together
form a powerful classifier. We test our dual-stream network on the task of
buildings segmentation in aerial images and obtain state-of-the-art results on
the Massachusetts Buildings Dataset. We study the relative importance of local
appearance versus the larger scene, as well as their performance in combination
on three new buildings datasets. We clearly demonstrate the effectiveness of
visual context in conjunction with deep neural networks for aerial image
understanding.
Reservoir computing for spatiotemporal signal classification without trained output weights
Authors: Ashley Prater
Date: 2016-04-11 00:00:00
Tasks: Classification, Clustering, General Classification

Reservoir computing is a recently introduced machine learning paradigm that
has been shown to be well-suited for the processing of spatiotemporal data.
Rather than training the network node connections and weights via
backpropagation in traditional recurrent neural networks, reservoirs instead
have fixed connections and weights among the `hidden layer' nodes, and
traditionally only the weights to the output layer of neurons are trained using
linear regression. We claim that for signal classification tasks one may forgo
the weight training step entirely and instead use a simple supervised
clustering method based upon principal components of norms of reservoir states.
The proposed method is mathematically analyzed and explored through numerical
experiments on real-world data. The examples demonstrate that the proposed may
outperform the traditional trained output weight approach in terms of
classification accuracy and sensitivity to reservoir parameters.
Dual Purpose Hashing
Authors: Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2016-07-19 00:00:00
Tasks: Attribute, Image Retrieval, Retrieval

Recent years have seen more and more demand for a unified framework to
address multiple realistic image retrieval tasks concerning both category and
attributes. Considering the scale of modern datasets, hashing is favorable for
its low complexity. However, most existing hashing methods are designed to
preserve one single kind of similarity, thus improper for dealing with the
different tasks simultaneously. To overcome this limitation, we propose a new
hashing method, named Dual Purpose Hashing (DPH), which jointly preserves the
category and attribute similarities by exploiting the Convolutional Neural
Network (CNN) models to hierarchically capture the correlations between
category and attributes. Since images with both category and attribute labels
are scarce, our method is designed to take the abundant partially labelled
images on the Internet as training inputs. With such a framework, the binary
codes of new-coming images can be readily obtained by quantizing the network
outputs of a binary-like layer, and the attributes can be recovered from the
codes easily. Experiments on two large-scale datasets show that our dual
purpose hash codes can achieve comparable or even better performance than those
state-of-the-art methods specifically designed for each individual retrieval
task, while being more compact than the compared methods.
Dendritic Spine Shape Analysis: A Clustering Perspective
Authors: Muhammad Usman Ghani, Ertunc Erdil, Sumeyra Demir Kanik, Ali Ozgur Argunsah, Anna Felicity Hobbiss, Inbal Israely, Devrim Unay, Tolga Tasdizen, Mujdat Cetin
Date: 2016-07-19 00:00:00
Tasks: Clustering, General Classification

Functional properties of neurons are strongly coupled with their morphology.
Changes in neuronal activity alter morphological characteristics of dendritic
spines. First step towards understanding the structure-function relationship is
to group spines into main spine classes reported in the literature. Shape
analysis of dendritic spines can help neuroscientists understand the underlying
relationships. Due to unavailability of reliable automated tools, this analysis
is currently performed manually which is a time-intensive and subjective task.
Several studies on spine shape classification have been reported in the
literature, however, there is an on-going debate on whether distinct spine
shape classes exist or whether spines should be modeled through a continuum of
shape variations. Another challenge is the subjectivity and bias that is
introduced due to the supervised nature of classification approaches. In this
paper, we aim to address these issues by presenting a clustering perspective.
In this context, clustering may serve both confirmation of known patterns and
discovery of new ones. We perform cluster analysis on two-photon microscopic
images of spines using morphological, shape, and appearance based features and
gain insights into the spine shape analysis problem. We use histogram of
oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological
features, and intensity profile based features for cluster analysis. We use
x-means to perform cluster analysis that selects the number of clusters
automatically using the Bayesian information criterion (BIC). For all features,
this analysis produces 4 clusters and we observe the formation of at least one
cluster consisting of spines which are difficult to be assigned to a known
class. This observation supports the argument of intermediate shape types.
Supervised Transformer Network for Efficient Face Detection
Authors: Dong Chen, Gang Hua, Fang Wen, Jian Sun
Date: 2016-07-19 00:00:00
Tasks: CPU, Face Detection, Region Proposal, valid

Large pose variations remain to be a challenge that confronts real-word face
detection. We propose a new cascaded Convolutional Neural Network, dubbed the
name Supervised Transformer Network, to address this challenge. The first stage
is a multi-task Region Proposal Network (RPN), which simultaneously predicts
candidate face regions along with associated facial landmarks. The candidate
regions are then warped by mapping the detected facial landmarks to their
canonical positions to better normalize the face patterns. The second stage,
which is a RCNN, then verifies if the warped candidate regions are valid faces
or not. We conduct end-to-end learning of the cascaded network, including
optimizing the canonical positions of the facial landmarks. This supervised
learning of the transformations automatically selects the best scale to
differentiate face/non-face patterns. By combining feature maps from both
stages of the network, we achieve state-of-the-art detection accuracies on
several public benchmarks. For real-time performance, we run the cascaded
network only on regions of interests produced from a boosting cascade face
detector. Our detector runs at 30 FPS on a single CPU core for a VGA-resolution
image.
Hybrid Collaborative Filtering with Autoencoders
Authors: Florian Strub, Jeremie Mary, Romaric Gaudel
Date: 2016-03-02 00:00:00
Tasks: Collaborative Filtering, speech-recognition

Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework.
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Authors: Xiaojie Jin, Yunpeng Chen, Jian Dong, Jiashi Feng, Shuicheng Yan
Date: 2016-07-19 00:00:00
Tasks: Classification, General Classification, Scene Classification

Intermediate features at different layers of a deep neural network are known
to be discriminative for visual patterns of different complexities. However,
most existing works ignore such cross-layer heterogeneities when classifying
samples of different complexities. For example, if a training sample has
already been correctly classified at a specific layer with high confidence, we
argue that it is unnecessary to enforce rest layers to classify this sample
correctly and a better strategy is to encourage those layers to focus on other
samples.
  In this paper, we propose a layer-wise discriminative learning method to
enhance the discriminative capability of a deep network by allowing its layers
to work collaboratively for classification. Towards this target, we introduce
multiple classifiers on top of multiple layers. Each classifier not only tries
to correctly classify the features from its input layer, but also coordinates
with other classifiers to jointly maximize the final classification
performance. Guided by the other companion classifiers, each classifier learns
to concentrate on certain training examples and boosts the overall performance.
Allowing for end-to-end training, our method can be conveniently embedded into
state-of-the-art deep networks. Experiments with multiple popular deep
networks, including Network in Network, GoogLeNet and VGGNet, on scale-various
object classification benchmarks, including CIFAR100, MNIST and ImageNet, and
scene classification benchmarks, including MIT67, SUN397 and Places205,
demonstrate the effectiveness of our method. In addition, we also analyze the
relationship between the proposed method and classical conditional random
fields models.
Robust Automated Human Activity Recognition and its Application to Sleep Research
Authors: Aarti Sathyanarayana, Ferda Ofli, Luis Fernandes-Luque, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri
Date: 2016-07-17 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Sleep Quality

Human Activity Recognition (HAR) is a powerful tool for understanding human
behaviour. Applying HAR to wearable sensors can provide new insights by
enriching the feature set in health studies, and enhance the personalisation
and effectiveness of health, wellness, and fitness applications. Wearable
devices provide an unobtrusive platform for user monitoring, and due to their
increasing market penetration, feel intrinsic to the wearer. The integration of
these devices in daily life provide a unique opportunity for understanding
human health and wellbeing. This is referred to as the "quantified self"
movement. The analyses of complex health behaviours such as sleep,
traditionally require a time-consuming manual interpretation by experts. This
manual work is necessary due to the erratic periodicity and persistent
noisiness of human behaviour. In this paper, we present a robust automated
human activity recognition algorithm, which we call RAHAR. We test our
algorithm in the application area of sleep research by providing a novel
framework for evaluating sleep quality and examining the correlation between
the aforementioned and an individual's physical activity. Our results improve
the state-of-the-art procedure in sleep research by 15 percent for area under
ROC and by 30 percent for F1 score on average. However, application of RAHAR is
not limited to sleep analysis and can be used for understanding other health
problems such as obesity, diabetes, and cardiac diseases.
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods
Authors: Xiaojie Jin, Xiao-Tong Yuan, Jiashi Feng, Shuicheng Yan
Date: 2016-07-19 00:00:00
Tasks: Object Recognition

Deep neural networks have achieved remarkable success in a wide range of
practical problems. However, due to the inherent large parameter space, deep
models are notoriously prone to overfitting and difficult to be deployed in
portable devices with limited memory. In this paper, we propose an iterative
hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs).
An SDNN has much fewer parameters yet can achieve competitive or even better
performance than its full CNN counterpart. More concretely, the IHT approach
trains an SDNN through following two alternative phases: (I) perform hard
thresholding to drop connections with small activations and fine-tune the other
significant filters; (II)~re-activate the frozen connections and train the
entire network to improve its overall discriminative capability. We verify the
superiority of SDNNs in terms of efficiency and classification performance on
four benchmark object recognition datasets, including CIFAR-10, CIFAR-100,
MNIST and ImageNet. Experimental results clearly demonstrate that IHT can be
applied for training SDNN based on various CNN architectures such as NIN and
AlexNet.
A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet
Authors: Abhijit Adhikari, Shivang Singh, Deepjyoti Mondal, Biswanath Dutta, Animesh Dutta
Date: 2016-07-19 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Information content (IC) based measures for finding semantic similarity is
gaining preferences day by day. Semantics of concepts can be highly
characterized by information theory. The conventional way for calculating IC is
based on the probability of appearance of concepts in corpora. Due to data
sparseness and corpora dependency issues of those conventional approaches, a
new corpora independent intrinsic IC calculation measure has evolved. In this
paper, we mainly focus on such intrinsic IC model and several topological
aspects of the underlying ontology. Accuracy of intrinsic IC calculation and
semantic similarity measure rely on these aspects deeply. Based on these
analysis we propose an information theoretic framework which comprises an
intrinsic IC calculator and a semantic similarity model. Our approach is
compared with state of the art semantic similarity measures based on corpora
dependent IC calculation as well as intrinsic IC based methods using several
benchmark data set. We also compare our model with the related Edge based,
Feature based and Distributional approaches. Experimental results show that our
intrinsic IC model gives high correlation value when applied to different
semantic similarity models. Our proposed semantic similarity model also
achieves significant results when embedded with some state of the art IC models
including ours.
Discriminating between similar languages in Twitter using label propagation
Authors: Will Radford, Matthias Galle
Date: 2016-07-19 00:00:00

Identifying the language of social media messages is an important first step
in linguistic processing. Existing models for Twitter focus on content
analysis, which is successful for dissimilar language pairs. We propose a label
propagation approach that takes the social graph of tweet authors into account
as well as content to better tease apart similar languages. This results in
state-of-the-art shared task performance of $76.63\%$, $1.4\%$ higher than the
top system.
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
Authors: Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee
Date: 2016-03-16 00:00:00

Recently, convolutional neural networks (CNNs) have been used as a powerful
tool to solve many problems of machine learning and computer vision. In this
paper, we aim to provide insight on the property of convolutional neural
networks, as well as a generic method to improve the performance of many CNN
architectures. Specifically, we first examine existing CNN models and observe
an intriguing property that the filters in the lower layers form pairs (i.e.,
filters with opposite phase). Inspired by our observation, we propose a novel,
simple yet effective activation scheme called concatenated ReLU (CRelu) and
theoretically analyze its reconstruction property in CNNs. We integrate CRelu
into several state-of-the-art CNN architectures and demonstrate improvement in
their recognition performance on CIFAR-10/100 and ImageNet datasets with fewer
trainable parameters. Our results suggest that better understanding of the
properties of CNNs can lead to significant performance improvement with a
simple modification.