pwc-archive/papers-with-abstracts huggingface dataset:
Gradient-based Optimization for Regression in the Functional Tensor-Train Format
Authors: Alex A. Gorodetsky, John D. Jakeman
Date: 2018-01-03 00:00:00
Tasks: regression

We consider the task of low-multilinear-rank functional regression, i.e.,
learning a low-rank parametric representation of functions from scattered
real-valued data. Our first contribution is the development and analysis of an
efficient gradient computation that enables gradient-based optimization
procedures, including stochastic gradient descent and quasi-Newton methods, for
learning the parameters of a functional tensor-train (FT). The functional
tensor-train uses the tensor-train (TT) representation of low-rank arrays as an
ansatz for a class of low-multilinear-rank functions. The FT is represented by
a set of matrix-valued functions that contain a set of univariate functions,
and the regression task is to learn the parameters of these univariate
functions. Our second contribution demonstrates that using nonlinearly
parameterized univariate functions, e.g., symmetric kernels with moving
centers, within each core can outperform the standard approach of using a
linear expansion of basis functions. Our final contributions are new rank
adaptation and group-sparsity regularization procedures to minimize
overfitting. We use several benchmark problems to demonstrate at least an order
of magnitude lower accuracy with gradient-based optimization methods than
standard alternating least squares procedures in the low-sample number regime.
We also demonstrate an order of magnitude reduction in accuracy on a test
problem resulting from using nonlinear parameterizations over linear
parameterizations. Finally we compare regression performance with 22 other
nonparametric and parametric regression methods on 10 real-world data sets. We
achieve top-five accuracy for seven of the data sets and best accuracy for two
of the data sets. These rankings are the best amongst parametric models and
competetive with the best non-parametric methods.
On Evaluating and Comparing Open Domain Dialog Systems
Authors: Anu Venkatesh, Chandra Khatri, Ashwin Ram, Fenfei Guo, Raefer Gabriel, Ashish Nagar, Rohit Prasad, Ming Cheng, Behnam Hedayatnia, Angeliki Metallinou, Rahul Goel, Shaohua Yang, Anirudh Raju
Date: 2018-01-11 00:00:00
Tasks: Goal-Oriented Dialogue Systems, Open-Domain Dialog

Conversational agents are exploding in popularity. However, much work remains
in the area of non goal-oriented conversations, despite significant growth in
research interest over recent years. To advance the state of the art in
conversational AI, Amazon launched the Alexa Prize, a 2.5-million dollar
university competition where sixteen selected university teams built
conversational agents to deliver the best social conversational experience.
Alexa Prize provided the academic community with the unique opportunity to
perform research with a live system used by millions of users. The subjectivity
associated with evaluating conversations is key element underlying the
challenge of building non-goal oriented dialogue systems. In this paper, we
propose a comprehensive evaluation strategy with multiple metrics designed to
reduce subjectivity by selecting metrics which correlate well with human
judgement. The proposed metrics provide granular analysis of the conversational
agents, which is not captured in human ratings. We show that these metrics can
be used as a reasonable proxy for human judgment. We provide a mechanism to
unify the metrics for selecting the top performing agents, which has also been
applied throughout the Alexa Prize competition. To our knowledge, to date it is
the largest setting for evaluating agents with millions of conversations and
hundreds of thousands of ratings from users. We believe that this work is a
step towards an automatic evaluation process for conversational AIs.
Active Community Detection with Maximal Expected Model Change
Authors: Dan Kushnir, Benjamin Mirabelli
Date: 2018-01-11 00:00:00
Tasks: Active Learning, Community Detection, model, Stochastic Block Model

We present a novel active learning algorithm for community detection on networks. Our proposed algorithm uses a Maximal Expected Model Change (MEMC) criterion for querying network nodes label assignments. MEMC detects nodes that maximally change the community assignment likelihood model following a query. Our method is inspired by detection in the benchmark Stochastic Block Model (SBM), where we provide sample complexity analysis and empirical study with SBM and real network data for binary as well as for the multi-class settings. The analysis also covers the most challenging case of sparse degree and below-detection-threshold SBMs, where we observe a super-linear error reduction. MEMC is shown to be superior to the random selection baseline and other state-of-the-art active learners.
Topic-based Evaluation for Conversational Bots
Authors: Fenfei Guo, Angeliki Metallinou, Chandra Khatri, Anirudh Raju, Anu Venkatesh, Ashwin Ram
Date: 2018-01-11 00:00:00
Tasks: Diversity, Topic Classification

Dialog evaluation is a challenging problem, especially for non task-oriented
dialogs where conversational success is not well-defined. We propose to
evaluate dialog quality using topic-based metrics that describe the ability of
a conversational bot to sustain coherent and engaging conversations on a topic,
and the diversity of topics that a bot can handle. To detect conversation
topics per utterance, we adopt Deep Average Networks (DAN) and train a topic
classifier on a variety of question and query data categorized into multiple
topics. We propose a novel extension to DAN by adding a topic-word attention
table that allows the system to jointly capture topic keywords in an utterance
and perform topic classification. We compare our proposed topic based metrics
with the ratings provided by users and show that our metrics both correlate
with and complement human judgment. Our analysis is performed on tens of
thousands of real human-bot dialogs from the Alexa Prize competition and
highlights user expectations for conversational bots.
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Authors: Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao
Date: 2018-01-03 00:00:00
Tasks: Segmentation

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The
cup to disc ratio (CDR) plays an important role in the screening and diagnosis
of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD)
and optic cup (OC) from fundus images is a fundamental task. Most existing
methods segment them separately, and rely on hand-crafted visual feature from
fundus images. In this paper, we propose a deep learning architecture, named
M-Net, which solves the OD and OC segmentation jointly in a one-stage
multi-label system. The proposed M-Net mainly consists of multi-scale input
layer, U-shape convolutional network, side-output layer, and multi-label loss
function. The multi-scale input layer constructs an image pyramid to achieve
multiple level receptive field sizes. The U-shape convolutional network is
employed as the main body network structure to learn the rich hierarchical
representation, while the side-output layer acts as an early classifier that
produces a companion local prediction map for different scale layers. Finally,
a multi-label loss function is proposed to generate the final segmentation map.
For improving the segmentation performance further, we also introduce the polar
transformation, which provides the representation of the original image in the
polar coordinate system. The experiments show that our M-Net system achieves
state-of-the-art OD and OC segmentation result on ORIGA dataset.
Simultaneously, the proposed method also obtains the satisfactory glaucoma
screening performances with calculated CDR value on both ORIGA and SCES
datasets.
Network Model Selection Using Task-Focused Minimum Description Length
Authors: Ivan Brugere, Tanya Y. Berger-Wolf
Date: 2017-10-14 00:00:00
Tasks: Model Selection, Translation

Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented. Users of downstream network data may not even be
aware of these choices or their impacts. We propose a task-focused network
model selection methodology which addresses several key challenges. Our
approach constructs network models from underlying data and uses minimum
description length (MDL) criteria for selection. Our methodology measures
efficiency, a general and comparable measure of the network's performance of a
local (i.e. node-level) predictive task of interest. Selection on efficiency
favors parsimonious (e.g. sparse) models to avoid overfitting and can be
applied across arbitrary tasks and representations. We show stability,
sensitivity, and significance testing in our methodology.
Distributed Constraint Optimization Problems and Applications: A Survey
Authors: Ferdinando Fioretto, Enrico Pontelli, William Yeoh
Date: 2016-02-20 00:00:00
Tasks: General Classification, Survey

The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas.
Improved English to Russian Translation by Neural Suffix Prediction
Authors: Kai Song, Yue Zhang, Min Zhang, Weihua Luo
Date: 2018-01-11 00:00:00
Tasks: Machine Translation, NMT, Prediction, Translation

Neural machine translation (NMT) suffers a performance deficiency when a
limited vocabulary fails to cover the source or target side adequately, which
happens frequently when dealing with morphologically rich languages. To address
this problem, previous work focused on adjusting translation granularity or
expanding the vocabulary size. However, morphological information is relatively
under-considered in NMT architectures, which may further improve translation
quality. We propose a novel method, which can not only reduce data sparsity but
also model morphology through a simple but effective mechanism. By predicting
the stem and suffix separately during decoding, our system achieves an
improvement of up to 1.98 BLEU compared with previous work on English to
Russian translation. Our method is orthogonal to different NMT architectures
and stably gains improvements on various domains.
Deep Classification of Epileptic Signals
Authors: David Ahmedt-Aristizabal, Clinton Fookes, Kien Nguyen, Sridha Sridharan
Date: 2018-01-11 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Seizure Detection, Time Series, Time Series Analysis

Electrophysiological observation plays a major role in epilepsy evaluation.
However, human interpretation of brain signals is subjective and prone to
misdiagnosis. Automating this process, especially seizure detection relying on
scalp-based Electroencephalography (EEG) and intracranial EEG, has been the
focus of research over recent decades. Nevertheless, its numerous challenges
have inhibited a definitive solution. Inspired by recent advances in deep
learning, we propose a new classification approach for EEG time series based on
Recurrent Neural Networks (RNNs) via the use of Long-Short Term Memory (LSTM)
networks. The proposed deep network effectively learns and models
discriminative temporal patterns from EEG sequential data. Especially, the
features are automatically discovered from the raw EEG data without any
pre-processing step, eliminating humans from laborious feature design task. We
also show that, in the epilepsy scenario, simple architectures can achieve
competitive performance. Using simple architectures significantly benefits in
the practical scenario considering their low computation complexity and reduced
requirement for large training datasets. Using a public dataset, a multi-fold
cross-validation scheme exhibited an average validation accuracy of 95.54\% and
an average AUC of 0.9582 of the ROC curve among all sets defined in the
experiment. This work reinforces the benefits of deep learning to be further
attended in clinical applications and neuroscientific research.
Conversational AI: The Science Behind the Alexa Prize
Authors: Ashwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue
Date: 2018-01-11 00:00:00
Tasks: Management, Natural Language Understanding, Response Generation, speech-recognition, Speech Recognition

Conversational agents are exploding in popularity. However, much work remains
in the area of social conversation as well as free-form conversation over a
broad range of domains and topics. To advance the state of the art in
conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar
university competition where sixteen selected university teams were challenged
to build conversational agents, known as socialbots, to converse coherently and
engagingly with humans on popular topics such as Sports, Politics,
Entertainment, Fashion and Technology for 20 minutes. The Alexa Prize offers
the academic community a unique opportunity to perform research with a live
system used by millions of users. The competition provided university teams
with real user conversational data at scale, along with the user-provided
ratings and feedback augmented with annotations by the Alexa team. This enabled
teams to effectively iterate and make improvements throughout the competition
while being evaluated in real-time through live user interactions. To build
their socialbots, university teams combined state-of-the-art techniques with
novel strategies in the areas of Natural Language Understanding, Context
Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To
support the efforts of participating teams, the Alexa Prize team made
significant scientific and engineering investments to build and improve
Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice
User Experience, and tools for traffic management and scalability. This paper
outlines the advances created by the university teams as well as the Alexa
Prize team to achieve the common goal of solving the problem of Conversational
AI.
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
Authors: Zhengqiu He, Wenliang Chen, Zhenghua Li, Meishan Zhang, Wei zhang, Min Zhang
Date: 2018-01-11 00:00:00
Tasks: Relation, Relation Classification, Relation Extraction, Sentence, Sentence Embedding, Sentence-Embedding

Distant supervised relation extraction is an efficient approach to scale
relation extraction to very large corpora, and has been widely used to find
novel relational facts from plain text. Recent studies on neural relation
extraction have shown great progress on this task via modeling the sentences in
low-dimensional spaces, but seldom considered syntax information to model the
entities. In this paper, we propose to learn syntax-aware entity embedding for
neural relation extraction. First, we encode the context of entities on a
dependency tree as sentence-level entity embedding based on tree-GRU. Then, we
utilize both intra-sentence and inter-sentence attentions to obtain sentence
set-level entity embedding over all sentences containing the focus entity pair.
Finally, we combine both sentence embedding and entity embedding for relation
classification. We conduct experiments on a widely used real-world dataset and
the experimental results show that our model can make full use of all
informative instances and achieve state-of-the-art performance of relation
extraction.
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
Authors: Dawei Li, Xiaolong Wang, Deguang Kong
Date: 2017-08-16 00:00:00
Tasks: CPU, Model Compression

Deploying deep neural networks on mobile devices is a challenging task.
Current model compression methods such as matrix decomposition effectively
reduce the deployed model size, but still cannot satisfy real-time processing
requirement. This paper first discovers that the major obstacle is the
excessive execution time of non-tensor layers such as pooling and normalization
without tensor-like trainable parameters. This motivates us to design a novel
acceleration framework: DeepRebirth through "slimming" existing consecutive and
parallel non-tensor and tensor layers. The layer slimming is executed at
different substructures: (a) streamline slimming by merging the consecutive
non-tensor and tensor layer vertically; (b) branch slimming by merging
non-tensor and tensor branches horizontally. The proposed optimization
operations significantly accelerate the model execution and also greatly reduce
the run-time memory cost since the slimmed model architecture contains less
hidden layers. To maximally avoid accuracy loss, the parameters in new
generated layers are learned with layer-wise fine-tuning based on both
theoretical analysis and empirical verification. As observed in the experiment,
DeepRebirth achieves more than 3x speed-up and 2.5x run-time memory saving on
GoogLeNet with only 0.4% drop of top-5 accuracy on ImageNet. Furthermore, by
combining with other model compression techniques, DeepRebirth offers an
average of 65ms inference time on the CPU of Samsung Galaxy S6 with 86.5% top-5
accuracy, 14% faster than SqueezeNet which only has a top-5 accuracy of 80.5%.
Australia's long-term electricity demand forecasting using deep neural networks
Authors: Homayoun Hamedmoghadam, Nima Joorabloo, Mahdi Jalili
Date: 2018-01-07 00:00:00
Tasks: Demand Forecasting, Management

Accurate prediction of long-term electricity demand has a significant role in
demand side management and electricity network planning and operation. Demand
over-estimation results in over-investment in network assets, driving up the
electricity prices, while demand under-estimation may lead to under-investment
resulting in unreliable and insecure electricity. In this manuscript, we apply
deep neural networks to predict Australia's long-term electricity demand. A
stacked autoencoder is used in combination with multilayer perceptrons or
cascade-forward multilayer perceptrons to predict the nation-wide electricity
consumption rates for 1-24 months ahead of time. The experimental results show
that the deep structures have better performance than classical neural
networks, especially for 12-month to 24-month prediction horizon.
Unsupervised Part-of-Speech Induction
Authors: Omid Kashefi
Date: 2018-01-10 00:00:00
Tasks: Clustering, Part-Of-Speech Tagging, POS, POS Tagging

Part-of-Speech (POS) tagging is an old and fundamental task in natural
language processing. While supervised POS taggers have shown promising
accuracy, it is not always feasible to use supervised methods due to lack of
labeled data. In this project, we attempt to unsurprisingly induce POS tags by
iteratively looking for a recurring pattern of words through a hierarchical
agglomerative clustering process. Our approach shows promising results when
compared to the tagging results of the state-of-the-art unsupervised POS
taggers.
From Superpixel to Human Shape Modelling for Carried Object Detection
Authors: Farnoosh Ghadiri, Robert Bergevin, Guillaume-Alexandre Bilodeau
Date: 2018-01-10 00:00:00
Tasks: Object, object-detection, Object Detection, Superpixels

Detecting carried objects is one of the requirements for developing systems
to reason about activities involving people and objects. We present an approach
to detect carried objects from a single video frame with a novel method that
incorporates features from multiple scales. Initially, a foreground mask in a
video frame is segmented into multi-scale superpixels. Then the human-like
regions in the segmented area are identified by matching a set of extracted
features from superpixels against learned features in a codebook. A carried
object probability map is generated using the complement of the matching
probabilities of superpixels to human-like regions and background information.
A group of superpixels with high carried object probability and strong edge
support is then merged to obtain the shape of the carried object. We applied
our method to two challenging datasets, and results show that our method is
competitive with or better than the state-of-the-art.
Real-time deep hair matting on mobile devices
Authors: Alex Levinshtein, Cheng Chang, Edmund Phung, Irina Kezele, Wenzhangzhi Guo, Parham Aarabi
Date: 2017-12-19 00:00:00
Tasks: Image Matting, Segmentation, Virtual Try-on

Augmented reality is an emerging technology in many application domains.
Among them is the beauty industry, where live virtual try-on of beauty products
is of great importance. In this paper, we address the problem of live hair
color augmentation. To achieve this goal, hair needs to be segmented quickly
and accurately. We show how a modified MobileNet CNN architecture can be used
to segment the hair in real-time. Instead of training this network using large
amounts of accurate segmentation data, which is difficult to obtain, we use
crowd sourced hair segmentation data. While such data is much simpler to
obtain, the segmentations there are noisy and coarse. Despite this, we show how
our system can produce accurate and fine-detailed hair mattes, while running at
over 30 fps on an iPad Pro tablet.
Segment-based Methods for Facial Attribute Detection from Partial Faces
Authors: Upal Mahbub, Sayantan Sarkar, Rama Chellappa
Date: 2018-01-10 00:00:00
Tasks: Attribute

State-of-the-art methods of attribute detection from faces almost always
assume the presence of a full, unoccluded face. Hence, their performance
degrades for partially visible and occluded faces. In this paper, we introduce
SPLITFACE, a deep convolutional neural network-based method that is explicitly
designed to perform attribute detection in partially occluded faces. Taking
several facial segments and the full face as input, the proposed method takes a
data driven approach to determine which attributes are localized in which
facial segments. The unique architecture of the network allows each attribute
to be predicted by multiple segments, which permits the implementation of
committee machine techniques for combining local and global decisions to boost
performance. With access to segment-based predictions, SPLITFACE can predict
well those attributes which are localized in the visible parts of the face,
without having to rely on the presence of the whole face. We use the CelebA and
LFWA facial attribute datasets for standard evaluations. We also modify both
datasets, to occlude the faces, so that we can evaluate the performance of
attribute detection algorithms on partial faces. Our evaluation shows that
SPLITFACE significantly outperforms other recent methods especially for partial
faces.