pwc-archive/papers-with-abstracts huggingface dataset:
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
Authors: Tom Sercu, Vaibhava Goel
Date: 2016-11-28 00:00:00
Tasks: Computational Efficiency, General Classification, Language Modeling, Language Modelling, Prediction, speech-recognition, Speech Recognition

In computer vision pixelwise dense prediction is the task of predicting a
label for each pixel in the image. Convolutional neural networks achieve good
performance on this task, while being computationally efficient. In this paper
we carry these ideas over to the problem of assigning a sequence of labels to a
set of speech frames, a task commonly known as framewise classification. We
show that dense prediction view of framewise classification offers several
advantages and insights, including computational efficiency and the ability to
apply batch normalization. When doing dense prediction we pay specific
attention to strided pooling in time and introduce an asymmetric dilated
convolution, called time-dilated convolution, that allows for efficient and
elegant implementation of pooling in time. We show results using time-dilated
convolutions in a very deep VGG-style CNN with batch normalization on the Hub5
Switchboard-2000 benchmark task. With a big n-gram language model, we achieve
7.7% WER which is the best single model single-pass performance reported so
far.
Single Image Action Recognition using Semantic Body Part Actions
Authors: Zhichen Zhao, Huimin Ma, ShaoDi You
Date: 2016-12-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, Temporal Action Localization

In this paper, we propose a novel single image action recognition algorithm
which is based on the idea of semantic body part actions. Unlike existing
bottom up methods, we argue that the human action is a combination of
meaningful body part actions. In detail, we divide human body into five parts:
head, torso, arms, hands and legs. And for each of the body parts, we define
several semantic body part actions, e.g., hand holding, hand waving. These
semantic body part actions are strongly related to the body actions, e.g.,
writing, and jogging. Based on the idea, we propose a deep neural network based
system: first, body parts are localized by a Semi-FCN network. Second, for each
body parts, a Part Action Res-Net is used to predict semantic body part
actions. And finally, we use SVM to fuse the body part actions and predict the
entire body action. Experiments on two dataset: PASCAL VOC 2012 and Stanford-40
report mAP improvement from the state-of-the-art by 3.8% and 2.6% respectively.
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
Authors: Zhiguang Wang, Weizhong Yan, Tim Oates
Date: 2016-11-20 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Classification, Time-Series Few-Shot Learning with Heterogeneous Channels

We propose a simple but strong baseline for time series classification from
scratch with deep neural networks. Our proposed baseline models are pure
end-to-end without any heavy preprocessing on the raw data or feature crafting.
The proposed Fully Convolutional Network (FCN) achieves premium performance to
other state-of-the-art approaches and our exploration of the very deep neural
networks with the ResNet structure is also competitive. The global average
pooling in our convolutional model enables the exploitation of the Class
Activation Map (CAM) to find out the contributing region in the raw data for
the specific labels. Our models provides a simple choice for the real world
application and a good starting point for the future research. An overall
analysis is provided to discuss the generalization capability of our models,
learned features, network structures and the classification semantics.
Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion
Authors: Hu Xu, Lei Shu, Jingyuan Zhang, Philip S. Yu
Date: 2016-12-14 00:00:00
Tasks: Community Question Answering, General Classification, Question Answering

Product Community Question Answering (PCQA) provides useful information about
products and their features (aspects) that may not be well addressed by product
descriptions and reviews. We observe that a product's compatibility issues with
other products are frequently discussed in PCQA and such issues are more
frequently addressed in accessories, i.e., via a yes/no question "Does this
mouse work with windows 10?". In this paper, we address the problem of
extracting compatible and incompatible products from yes/no questions in PCQA.
This problem can naturally have a two-stage framework: first, we perform
Complementary Entity (product) Recognition (CER) on yes/no questions; second,
we identify the polarities of yes/no answers to assign the complementary
entities a compatibility label (compatible, incompatible or unknown). We
leverage an existing unsupervised method for the first stage and a 3-class
classifier by combining a distant PU-learning method (learning from positive
and unlabeled examples) together with a binary classifier for the second stage.
The benefit of using distant PU-learning is that it can help to expand more
implicit yes/no answers without using any human annotated data. We conduct
experiments on 4 products to show that the proposed method is effective.
Constrained Maximum Correntropy Adaptive Filtering
Authors: Siyuan Peng, Badong Chen, Lei Sun, Zhiping Lin, Wee Ser
Date: 2016-10-06 00:00:00

Constrained adaptive filtering algorithms inculding constrained least mean
square (CLMS), constrained affine projection (CAP) and constrained recursive
least squares (CRLS) have been extensively studied in many applications. Most
existing constrained adaptive filtering algorithms are developed under mean
square error (MSE) criterion, which is an ideal optimality criterion under
Gaussian noises. This assumption however fails to model the behavior of
non-Gaussian noises found in practice. Motivated by the robustness and
simplicity of maximum correntropy criterion (MCC) in non-Gaussian impulsive
noises, this paper proposes a new adaptive filtering algorithm called
constrained maximum correntropy criterion (CMCC). Specifically, CMCC
incorporates a linear constraint into a MCC filter to solve a constrained
optimization problem explicitly. The proposed adaptive filtering algorithm is
easy to implement and has low computational complexity, and in terms of
convergence accuracy (say lower mean square deviation) and stability, can
significantly outperform those MSE based constrained adaptive algorithms in
presence of heavy-tailed impulsive noises. Additionally, the mean square
convergence behaviors are studied under energy conservation relation, and a
sufficient condition to ensure the mean square convergence and the steady-state
mean square deviation (MSD) of the proposed algorithm are obtained. Simulation
results confirm the theoretical predictions under both Gaussian and non-
Gaussian noises, and demonstrate the excellent performance of the novel
algorithm by comparing it with other conventional methods.
Web-based Argumentation
Authors: Kenrick
Date: 2016-12-14 00:00:00

Assumption-Based Argumentation (ABA) is an argumentation framework that has
been proposed in the late 20th century. Since then, there was still no solver
implemented in a programming language which is easy to setup and no solver have
been interfaced to the web, which impedes the interests of the public. This
project aims to implement an ABA solver in a modern programming language that
performs reasonably well and interface it to the web for easier access by the
public. This project has demonstrated the novelty of development of an ABA
solver, that computes conflict-free, stable, admissible, grounded, ideal, and
complete semantics, in Python programming language which can be used via an
easy-to-use web interface for visualization of the argument and dispute trees.
Experiments were conducted to determine the project's best configurations and
to compare this project with proxdd, a state-of-the-art ABA solver, which has
no web interface and computes less number of semantics. From the results of the
experiments, this project's best configuration is achieved by utilizing
"pickle" technique and tree caching technique. Using this project's best
configuration, this project achieved a lower average runtime compared to
proxdd. On other aspect, this project encountered more cases with exceptions
compared to proxdd, which might be caused by this project computing more
semantics and hence requires more resources to do so. Hence, it can be said
that this project run comparably well to the state-of-the-art ABA solver
proxdd. Future works of this project include computational complexity analysis
and efficiency analysis of algorithms implemented, implementation of more
semantics in argumentation framework, and usability testing of the web
interface.
Sparse Factorization Layers for Neural Networks with Limited Supervision
Authors: Parker Koch, Jason J. Corso
Date: 2016-12-14 00:00:00
Tasks: Denoising, Dictionary Learning

Whereas CNNs have demonstrated immense progress in many vision problems, they
suffer from a dependence on monumental amounts of labeled training data. On the
other hand, dictionary learning does not scale to the size of problems that
CNNs can handle, despite being very effective at low-level vision tasks such as
denoising and inpainting. Recently, interest has grown in adapting dictionary
learning methods for supervised tasks such as classification and inverse
problems. We propose two new network layers that are based on dictionary
learning: a sparse factorization layer and a convolutional sparse factorization
layer, analogous to fully-connected and convolutional layers, respectively.
Using our derivations, these layers can be dropped in to existing CNNs, trained
together in an end-to-end fashion with back-propagation, and leverage
semisupervision in ways classical CNNs cannot. We experimentally compare
networks with these two new layers against a baseline CNN. Our results
demonstrate that networks with either of the sparse factorization layers are
able to outperform classical CNNs when supervised data are few. They also show
performance improvements in certain tasks when compared to the CNN with no
sparse factorization layers with the same exact number of parameters.
Nonparametric Detection of Anomalous Data Streams
Authors: Shaofeng Zou, Yingbin Liang, H. Vincent Poor, Xinghua Shi
Date: 2014-04-25 00:00:00
Tasks: Two-sample testing

A nonparametric anomalous hypothesis testing problem is investigated, in
which there are totally n sequences with s anomalous sequences to be detected.
Each typical sequence contains m independent and identically distributed
(i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence
contains m i.i.d. samples drawn from a distribution q that is distinct from p.
The distributions p and q are assumed to be unknown in advance.
Distribution-free tests are constructed using maximum mean discrepancy as the
metric, which is based on mean embeddings of distributions into a reproducing
kernel Hilbert space. The probability of error is bounded as a function of the
sample size m, the number s of anomalous sequences and the number n of
sequences. It is then shown that with s known, the constructed test is
exponentially consistent if m is greater than a constant factor of log n, for
any p and q, whereas with s unknown, m should has an order strictly greater
than log n. Furthermore, it is shown that no test can be consistent for
arbitrary p and q if m is less than a constant factor of log n, thus the
order-level optimality of the proposed test is established. Numerical results
are provided to demonstrate that our tests outperform (or perform as well as)
the tests based on other competitive approaches under various cases.
Analysis of proposed PDE-based underwater image enhancement algorithms
Authors: U. A. Nnolim
Date: 2016-12-14 00:00:00
Tasks: Image Enhancement

This report describes the experimental analysis of proposed underwater image
enhancement algorithms based on partial differential equations (PDEs). The
algorithms perform simultaneous smoothing and enhancement due to the
combination of both processes within the PDE-formulation. The framework enables
the incorporation of suitable colour and contrast enhancement algorithms within
one unified functional. Additional modification of the formulation includes the
combination of the popular Contrast Limited Adaptive Histogram Equalization
(CLAHE) with the proposed approach. This modification enables the hybrid
algorithm to provide both local enhancement (due to the CLAHE) and global
enhancement (due to the proposed contrast term). Additionally, the CLAHE clip
limit parameter is computed dynamically in each iteration and used to gauge the
amount of local enhancement performed by the CLAHE within the formulation. This
enables the algorithm to reduce or prevent the enhancement of noisy artifacts,
which if present, are also smoothed out by the anisotropic diffusion term
within the PDE formulation. In other words, the modified algorithm combines the
strength of the CLAHE, AD and the contrast term while minimizing their
weaknesses. Ultimately, the system is optimized using image data metrics for
automated enhancement and compromise between visual and quantitative results.
Experiments indicate that the proposed algorithms perform a series of functions
such as illumination correction, colour enhancement correction and restoration,
contrast enhancement and noise suppression. Moreover, the proposed approaches
surpass most other conventional algorithms found in the literature.
Fast Stability Scanning for Future Grid Scenario Analysis
Authors: Ruidong Liu, Gregor Verbic, Jin Ma
Date: 2016-12-14 00:00:00
Tasks: Clustering, feature selection, Time Series, Time Series Analysis

Future grid scenario analysis requires a major departure from conventional
power system planning, where only a handful of most critical conditions is
typically analyzed. To capture the inter-seasonal variations in renewable
generation of a future grid scenario necessitates the use of computationally
intensive time-series analysis. In this paper, we propose a planning framework
for fast stability scanning of future grid scenarios using a novel feature
selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm.
To achieve the computational speed-up, the stability analysis is performed only
on small number of representative cluster centroids instead of on the full set
of operating conditions. As a case study, we perform small-signal stability and
steady-state voltage stability scanning of a simplified model of the Australian
National Electricity Market with significant penetration of renewable
generation. The simulation results show the effectiveness of the proposed
approach. Compared to an exhaustive time series scanning, the proposed
framework reduced the computational burden up to ten times, with an acceptable
level of accuracy.
An argumentative agent-based model of scientific inquiry
Authors: AnneMarie Borg, Daniel Frey, Dunja Šešelja, Christian Straßer
Date: 2016-12-13 00:00:00
Tasks: Abstract Argumentation, model

In this paper we present an agent-based model (ABM) of scientific inquiry
aimed at investigating how different social networks impact the efficiency of
scientists in acquiring knowledge. As such, the ABM is a computational tool for
tackling issues in the domain of scientific methodology and science policy. In
contrast to existing ABMs of science, our model aims to represent the
argumentative dynamics that underlies scientific practice. To this end we
employ abstract argumentation theory as the core design feature of the model.
This helps to avoid a number of problematic idealizations which are present in
other ABMs of science and which impede their relevance for actual scientific
practice.
Surprisal-Driven Zoneout
Authors: Kamil Rocki, Tomasz Kornuta, Tegan Maharaj
Date: 2016-10-24 00:00:00

We propose a novel method of regularization for recurrent neural networks
called suprisal-driven zoneout. In this method, states zoneout (maintain their
previous value rather than updating), when the suprisal (discrepancy between
the last state's prediction and target) is small. Thus regularization is
adaptive and input-driven on a per-neuron basis. We demonstrate the
effectiveness of this idea by achieving state-of-the-art bits per character of
1.31 on the Hutter Prize Wikipedia dataset, significantly reducing the gap to
the best known highly-engineered compression methods.
Improving Neural Language Models with a Continuous Cache
Authors: Edouard Grave, Armand Joulin, Nicolas Usunier
Date: 2016-12-13 00:00:00
Tasks: Language Modeling, Language Modelling

We propose an extension to neural network language models to adapt their
prediction to the recent history. Our model is a simplified version of memory
augmented networks, which stores past hidden activations as memory and accesses
them through a dot product with the current hidden activation. This mechanism
is very efficient and scales to very large memory sizes. We also draw a link
between the use of external memory in neural network and cache models used with
count based language models. We demonstrate on several language model datasets
that our approach performs significantly better than recent memory augmented
networks.
Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks
Authors: Bethany Lusch, Jake Weholt, Pedro D. Maia, J. Nathan Kutz
Date: 2016-12-13 00:00:00
Tasks: Prognosis

The accurate diagnosis and assessment of neurodegenerative disease and
traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and
functional deficits due to focal axonal swellings (FAS), but it is difficult to
deliver a prognosis due to our limited ability to assess damaged neurons at a
cellular level in vivo. We simulate the effects of neurodegenerative disease
and TBI using convolutional neural networks (CNNs) as our model of cognition.
We utilize biophysically relevant statistical data on FAS to damage the
connections in CNNs in a functionally relevant way. We incorporate energy
constraints on the brain by pruning the CNNs to be less over-engineered.
Qualitatively, we demonstrate that damage leads to human-like mistakes. Our
experiments also provide quantitative assessments of how accuracy is affected
by various types and levels of damage. The deficit resulting from a fixed
amount of damage greatly depends on which connections are randomly injured,
providing intuition for why it is difficult to predict impairments. There is a
large degree of subjectivity when it comes to interpreting cognitive deficits
from complex systems such as the human brain. However, we provide important
insight and a quantitative framework for disorders in which FAS are implicated.
User Model-Based Intent-Aware Metrics for Multilingual Search Evaluation
Authors: Alexey Drutsa, Andrey Shutovich, Philipp Pushnyakov, Evgeniy Krokhalyov, Gleb Gusev, Pavel Serdyukov
Date: 2016-12-13 00:00:00

Despite the growing importance of multilingual aspect of web search, no
appropriate offline metrics to evaluate its quality are proposed so far. At the
same time, personal language preferences can be regarded as intents of a query.
This approach translates the multilingual search problem into a particular task
of search diversification. Furthermore, the standard intent-aware approach
could be adopted to build a diversified metric for multilingual search on the
basis of a classical IR metric such as ERR. The intent-aware approach estimates
user satisfaction under a user behavior model. We show however that the
underlying user behavior models is not realistic in the multilingual case, and
the produced intent-aware metric do not appropriately estimate the user
satisfaction. We develop a novel approach to build intent-aware user behavior
models, which overcome these limitations and convert to quality metrics that
better correlate with standard online metrics of user satisfaction.
Inferring object rankings based on noisy pairwise comparisons from multiple annotators
Authors: Rahul Gupta, Shrikanth Narayanan
Date: 2016-12-13 00:00:00

Ranking a set of objects involves establishing an order allowing for
comparisons between any pair of objects in the set. Oftentimes, due to the
unavailability of a ground truth of ranked orders, researchers resort to
obtaining judgments from multiple annotators followed by inferring the ground
truth based on the collective knowledge of the crowd. However, the aggregation
is often ad-hoc and involves imposing stringent assumptions in inferring the
ground truth (e.g. majority vote). In this work, we propose
Expectation-Maximization (EM) based algorithms that rely on the judgments from
multiple annotators and the object attributes for inferring the latent ground
truth. The algorithm learns the relation between the latent ground truth and
object attributes as well as annotator specific probabilities of flipping, a
metric to assess annotator quality. We further extend the EM algorithm to allow
for a variable probability of flipping based on the pair of objects at hand. We
test our algorithms on two data sets with synthetic annotations and investigate
the impact of annotator quality and quantity on the inferred ground truth. We
also obtain the results on two other data sets with annotations from
machine/human annotators and interpret the output trends based on the data
characteristics.
You Are What You Eat... Listen to, Watch, and Read
Authors: Mason Bretan
Date: 2016-12-13 00:00:00
Tasks: Recommendation Systems

This article describes a data driven method for deriving the relationship
between personality and media preferences. A qunatifiable representation of
such a relationship can be leveraged for use in recommendation systems and
ameliorate the "cold start" problem. Here, the data is comprised of an original
collection of 1,316 Okcupid dating profiles. Of these profiles, 800 are labeled
with one of 16 possible Myers-Briggs Type Indicators (MBTI). A personality
specific topic model describing a person's favorite books, movies, shows,
music, and food was generated using latent Dirichlet allocation (LDA). There
were several significant findings, for example, intuitive thinking types
preferred sci-fi/fantasy entertainment, extraversion correlated positively with
upbeat dance music, and jazz, folk, and international cuisine correlated
positively with those characterized by openness to experience. Many other
correlations confirmed previous findings describing the relationship among
personality, writing style, and personal preferences. (For complete
word/personality type assocations see the Appendix).
Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
Authors: Radu Soricut, Nan Ding
Date: 2016-12-13 00:00:00
Tasks: Dataset Generation, Machine Reading Comprehension, Reading Comprehension

We present a dual contribution to the task of machine reading-comprehension:
a technique for creating large-sized machine-comprehension (MC) datasets using
paragraph-vector models; and a novel, hybrid neural-network architecture that
combines the representation power of recurrent neural networks with the
discriminative power of fully-connected multi-layered networks. We use the
MC-dataset generation technique to build a dataset of around 2 million
examples, for which we empirically determine the high-ceiling of human
performance (around 91% accuracy), as well as the performance of a variety of
computer models. Among all the models we have experimented with, our hybrid
neural-network architecture achieves the highest performance (83.2% accuracy).
The remaining gap to the human-performance ceiling provides enough room for
future model improvements.