pwc-archive/papers-with-abstracts huggingface dataset:
Deep Health Care Text Classification
Authors: Vinayakumar R, Barathi Ganesh HB, Anand Kumar M, Soman Kp
Date: 2017-10-23 00:00:00
Tasks: Classification, Feature Engineering, General Classification, text-classification, Text Classification

Health related social media mining is a valuable apparatus for the early
recognition of the diverse antagonistic medicinal conditions. Mostly, the
existing methods are based on machine learning with knowledge-based learning.
This working note presents the Recurrent neural network (RNN) and Long
short-term memory (LSTM) based embedding for automatic health text
classification in the social media mining. For each task, two systems are built
and that classify the tweet at the tweet level. RNN and LSTM are used for
extracting features and non-linear activation function at the last layer
facilitates to distinguish the tweets of different categories. The experiments
are conducted on 2nd Social Media Mining for Health Applications Shared Task at
AMIA 2017. The experiment results are considerable; however the proposed method
is appropriate for the health text classification. This is primarily due to the
reason that, it doesn't rely on any feature engineering mechanisms.
Listening to the World Improves Speech Command Recognition
Authors: Brian McMahan, Delip Rao
Date: 2017-10-23 00:00:00
Tasks: Environmental Sound Classification, General Classification, Sound Classification, Transfer Learning

We study transfer learning in convolutional network architectures applied to
the task of recognizing audio, such as environmental sound events and speech
commands. Our key finding is that not only is it possible to transfer
representations from an unrelated task like environmental sound classification
to a voice-focused task like speech command recognition, but also that doing so
improves accuracies significantly. We also investigate the effect of increased
model capacity for transfer learning audio, by first validating known results
from the field of Computer Vision of achieving better accuracies with
increasingly deeper networks on two audio datasets: UrbanSound8k and the newly
released Google Speech Commands dataset. Then we propose a simple multiscale
input representation using dilated convolutions and show that it is able to
aggregate larger contexts and increase classification performance. Further, the
models trained using a combination of transfer learning and multiscale input
representations need only 40% of the training data to achieve similar
accuracies as a freshly trained model with 100% of the training data. Finally,
we demonstrate a positive interaction effect for the multiscale input and
transfer learning, making a case for the joint application of the two
techniques.
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Authors: Wang Ling, Dani Yogatama, Chris Dyer, Phil Blunsom
Date: 2017-05-11 00:00:00
Tasks: Program induction

Solving algebraic word problems requires executing a series of arithmetic
operations---a program---to obtain a final answer. However, since programs can
be arbitrarily complicated, inducing them directly from question-answer pairs
is a formidable challenge. To make this task more feasible, we solve these
problems by generating answer rationales, sequences of natural language and
human-readable mathematical expressions that derive the final answer through a
series of small steps. Although rationales do not explicitly specify programs,
they provide a scaffolding for their structure via intermediate milestones. To
evaluate our approach, we have created a new 100,000-sample dataset of
questions, answers and rationales. Experimental results show that indirect
supervision of program learning via answer rationales is a promising strategy
for inducing arithmetic programs.
Large Linear Multi-output Gaussian Process Learning
Authors: Vladimir Feinberg, Li-Fang Cheng, Kai Li, Barbara E. Engelhardt
Date: 2017-05-30 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

Gaussian processes (GPs), or distributions over arbitrary functions in a
continuous domain, can be generalized to the multi-output case: a linear model
of coregionalization (LMC) is one approach. LMCs estimate and exploit
correlations across the multiple outputs. While model estimation can be
performed efficiently for single-output GPs, these assume stationarity, but in
the multi-output case the cross-covariance interaction is not stationary. We
propose Large Linear GP (LLGP), which circumvents the need for stationarity by
inducing structure in the LMC kernel through a common grid of inputs shared
between outputs, enabling optimization of GP hyperparameters for
multi-dimensional outputs and low-dimensional inputs. When applied to synthetic
two-dimensional and real time series data, we find our theoretical improvement
relative to the current solutions for multi-output GPs is realized with LLGP
reducing training time while improving or maintaining predictive mean accuracy.
Moreover, by using a direct likelihood approximation rather than a variational
one, model confidence estimates are significantly improved.
Automatic Identification of Scenedesmus Polymorphic Microalgae from Microscopic Images
Authors: Jhony-Heriberto Giraldo-Zuluaga, Geman Diez, Alexander Gomez, Tatiana Martinez, Mariana Peñuela Vasquez, Jesus Francisco Vargas Bonilla, Augusto Salazar
Date: 2016-12-21 00:00:00

Microalgae counting is used to measure biomass quantity. Usually, it is
performed in a manual way using a Neubauer chamber and expert criterion, with
the risk of a high error rate. This paper addresses the methodology for
automatic identification of Scenedesmus microalgae (used in the methane
production and food industry) and applies it to images captured by a digital
microscope. The use of contrast adaptive histogram equalization for
pre-processing, and active contours for segmentation are presented. The
calculation of statistical features (Histogram of Oriented Gradients, Hu and
Zernike moments) with texture features (Haralick and Local Binary Patterns
descriptors) are proposed for algae characterization. Scenedesmus algae can
build coenobia consisting of 1, 2, 4 and 8 cells. The amount of algae of each
coenobium helps to determine the amount of lipids, proteins, and other
substances in a given sample of a algae crop. The knowledge of the quantity of
those elements improves the quality of bioprocess applications. Classification
of coenobia achieves accuracies of 98.63% and 97.32% with Support Vector
Machine (SVM) and Artificial Neural Network (ANN), respectively. According to
the results it is possible to consider the proposed methodology as an
alternative to the traditional technique for algae counting. The database used
in this paper is publicly available for download.
Nonparametric Online Regression while Learning the Metric
Authors: Ilja Kuzborskij, Nicolò Cesa-Bianchi
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: regression

We study algorithms for online nonparametric regression that learn the
directions along which the regression function is smoother. Our algorithm
learns the Mahalanobis metric based on the gradient outer product matrix
$\boldsymbol{G}$ of the regression function (automatically adapting to the
effective rank of this matrix), while simultaneously bounding the regret ---on
the same data sequence--- in terms of the spectrum of $\boldsymbol{G}$. As a
preliminary step in our analysis, we extend a nonparametric online learning
algorithm by Hazan and Megiddo enabling it to compete against functions whose
Lipschitzness is measured with respect to an arbitrary Mahalanobis metric.
Improving Efficiency in Convolutional Neural Network with Multilinear Filters
Authors: Dat Thanh Tran, Alexandros Iosifidis, Moncef Gabbouj
Date: 2017-09-28 00:00:00

The excellent performance of deep neural networks has enabled us to solve
several automatization problems, opening an era of autonomous devices. However,
current deep net architectures are heavy with millions of parameters and
require billions of floating point operations. Several works have been
developed to compress a pre-trained deep network to reduce memory footprint
and, possibly, computation. Instead of compressing a pre-trained network, in
this work, we propose a generic neural network layer structure employing
multilinear projection as the primary feature extractor. The proposed
architecture requires several times less memory as compared to the traditional
Convolutional Neural Networks (CNN), while inherits the similar design
principles of a CNN. In addition, the proposed architecture is equipped with
two computation schemes that enable computation reduction or scalability.
Experimental results show the effectiveness of our compact projection that
outperforms traditional CNN, while requiring far fewer parameters.
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Authors: Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Yan Tong
Date: 2017-10-09 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Over the past few years, Convolutional Neural Networks (CNNs) have shown
promise on facial expression recognition. However, the performance degrades
dramatically under real-world settings due to variations introduced by subtle
facial appearance changes, head pose variations, illumination changes, and
occlusions.
  In this paper, a novel island loss is proposed to enhance the discriminative
power of the deeply learned features. Specifically, the IL is designed to
reduce the intra-class variations while enlarging the inter-class differences
simultaneously. Experimental results on four benchmark expression databases
have demonstrated that the CNN with the proposed island loss (IL-CNN)
outperforms the baseline CNN models with either traditional softmax loss or the
center loss and achieves comparable or better performance compared with the
state-of-the-art methods for facial expression recognition.
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
Authors: Anestis Tsakmalis, Symeon Chatzinotas, Björn Ottersten
Date: 2017-10-23 00:00:00
Tasks: Active Learning

In this paper, a sequential probing method for interference constraint
learning is proposed to allow a centralized Cognitive Radio Network (CRN)
accessing the frequency band of a Primary User (PU) in an underlay cognitive
scenario with a designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
the binary ACK/NACK packet. This feedback indicates whether the probing-induced
interference is harmful or not and can be used to learn the PU interference
constraint. The cognitive part of this sequential probing process is the
selection of the power levels of the Secondary Users (SUs) which aims to learn
the PU interference constraint with a minimum number of probing attempts while
setting a limit on the number of harmful probing-induced interference events or
equivalently of NACK packet observations over a time window. This constrained
design problem is studied within the Active Learning (AL) framework and an
optimal solution is derived and implemented with a sophisticated, accurate and
fast Bayesian Learning method, the Expectation Propagation (EP). The
performance of this solution is also demonstrated through numerical simulations
and compared with modified versions of AL techniques we developed in earlier
work.
Content Based Document Recommender using Deep Learning
Authors: Nishant Nikhil, Muktabh Mayank Srivastava
Date: 2017-10-23 00:00:00
Tasks: Deep Learning, Information Retrieval, Retrieval

With the recent advancements in information technology there has been a huge
surge in amount of data available. But information retrieval technology has not
been able to keep up with this pace of information generation resulting in over
spending of time for retrieving relevant information. Even though systems exist
for assisting users to search a database along with filtering and recommending
relevant information, but recommendation system which uses content of documents
for recommendation still have a long way to mature. Here we present a Deep
Learning based supervised approach to recommend similar documents based on the
similarity of content. We combine the C-DSSM model with Word2Vec distributed
representations of words to create a novel model to classify a document pair as
relevant/irrelavant by assigning a score to it. Using our model retrieval of
documents can be done in O(1) time and the memory complexity is O(n), where n
is number of documents.
Speeding-up the decision making of a learning agent using an ion trap quantum processor
Authors: Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich
Date: 2017-09-05 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

We report a proof-of-principle experimental demonstration of the quantum
speed-up for learning agents utilizing a small-scale quantum information
processor based on radiofrequency-driven trapped ions. The decision-making
process of a quantum learning agent within the projective simulation paradigm
for machine learning is implemented in a system of two qubits. The latter are
realized using hyperfine states of two frequency-addressed atomic ions exposed
to a static magnetic field gradient. We show that the deliberation time of this
quantum learning agent is quadratically improved with respect to comparable
classical learning agents. The performance of this quantum-enhanced learning
agent highlights the potential of scalable quantum processors taking advantage
of machine learning.
Generic 3D Representation via Pose Estimation and Matching
Authors: Amir R. Zamir, Tilman Wekel, Pulkit Argrawal, Colin Weil, Jitendra Malik, Silvio Savarese
Date: 2017-10-23 00:00:00
Tasks: Camera Pose Estimation, Object, Pose Estimation, Surface Normal Estimation

Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.
Testing the limits of unsupervised learning for semantic similarity
Authors: Richa Sharma, Muktabh Mayank Srivastava
Date: 2017-10-23 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings

Semantic Similarity between two sentences can be defined as a way to
determine how related or unrelated two sentences are. The task of Semantic
Similarity in terms of distributed representations can be thought to be
generating sentence embeddings (dense vectors) which take both context and
meaning of sentence in account. Such embeddings can be produced by multiple
methods, in this paper we try to evaluate LSTM auto encoders for generating
these embeddings. Unsupervised algorithms (auto encoders to be specific) just
try to recreate their inputs, but they can be forced to learn order (and some
inherent meaning to some extent) by creating proper bottlenecks. We try to
evaluate how properly can algorithms trained just on plain English Sentences
learn to figure out Semantic Similarity, without giving them any sense of what
meaning of a sentence is.
Investigating the feature collection for semantic segmentation via single skip connection
Authors: Jonghwa Yim, Kyung-Ah Sohn
Date: 2017-10-23 00:00:00
Tasks: Object, object-detection, Object Detection, Semantic Segmentation

Since the study of deep convolutional neural network became prevalent, one of
the important discoveries is that a feature map from a convolutional network
can be extracted before going into the fully connected layer and can be used as
a saliency map for object detection. Furthermore, the model can use features
from each different layer for accurate object detection: the features from
different layers can have different properties. As the model goes deeper, it
has many latent skip connections and feature maps to elaborate object
detection. Although there are many intermediate layers that we can use for
semantic segmentation through skip connection, still the characteristics of
each skip connection and the best skip connection for this task are uncertain.
Therefore, in this study, we exhaustively research skip connections of
state-of-the-art deep convolutional networks and investigate the
characteristics of the features from each intermediate layer. In addition, this
study would suggest how to use a recent deep neural network model for semantic
segmentation and it would therefore become a cornerstone for later studies with
the state-of-the-art network models.
Human-in-the-loop Artificial Intelligence
Authors: Fabio Massimo Zanzotto
Date: 2017-10-23 00:00:00

Little by little, newspapers are revealing the bright future that Artificial
Intelligence (AI) is building. Intelligent machines will help everywhere.
However, this bright future has a dark side: a dramatic job market contraction
before its unpredictable transformation. Hence, in a near future, large numbers
of job seekers will need financial support while catching up with these novel
unpredictable jobs. This possible job market crisis has an antidote inside. In
fact, the rise of AI is sustained by the biggest knowledge theft of the recent
years. Learning AI machines are extracting knowledge from unaware skilled or
unskilled workers by analyzing their interactions. By passionately doing their
jobs, these workers are digging their own graves.
  In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI)
as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward
aware and unaware knowledge producers with a different scheme: decisions of AI
systems generating revenues will repay the legitimate owners of the knowledge
used for taking those decisions. As modern Robin Hoods, HIT-AI researchers
should fight for a fairer Artificial Intelligence that gives back what it
steals.
Progressive Learning for Systematic Design of Large Neural Networks
Authors: Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Partha P. Mitra, Mikael Skoglund
Date: 2017-10-23 00:00:00

We develop an algorithm for systematic design of a large artificial neural
network using a progression property. We find that some non-linear functions,
such as the rectifier linear unit and its derivatives, hold the property. The
systematic design addresses the choice of network size and regularization of
parameters. The number of nodes and layers in network increases in progression
with the objective of consistently reducing an appropriate cost. Each layer is
optimized at a time, where appropriate parameters are learned using convex
optimization. Regularization parameters for convex optimization do not need a
significant manual effort for tuning. We also use random instances for some
weight matrices, and that helps to reduce the number of parameters we learn.
The developed network is expected to show good generalization power due to
appropriate regularization and use of random weights in the layers. This
expectation is verified by extensive experiments for classification and
regression problems, using standard databases.
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
Authors: Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Date: 2017-10-23 00:00:00

Visual exploration of high-dimensional real-valued datasets is a fundamental
task in exploratory data analysis (EDA). Existing methods use predefined
criteria to choose the representation of data. There is a lack of methods that
(i) elicit from the user what she has learned from the data and (ii) show
patterns that she does not know yet. We construct a theoretical model where
identified patterns can be input as knowledge to the system. The knowledge
syntax here is intuitive, such as "this set of points forms a cluster", and
requires no knowledge of maths. This background knowledge is used to find a
Maximum Entropy distribution of the data, after which the system provides the
user data projections in which the data and the Maximum Entropy distribution
differ the most, hence showing the user aspects of the data that are maximally
informative given the user's current knowledge. We provide an open source EDA
system with tailored interactive visualizations to demonstrate these concepts.
We study the performance of the system and present use cases on both synthetic
and real data. We find that the model and the prototype system allow the user
to learn information efficiently from various data sources and the system works
sufficiently fast in practice. We conclude that the information theoretic
approach to exploratory data analysis where patterns observed by a user are
formalized as constraints provides a principled, intuitive, and efficient basis
for constructing an EDA system.
Fast MCMC sampling algorithms on polytopes
Authors: Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu
Date: 2017-10-23 00:00:00

We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and
the John walk, for generating samples from the uniform distribution over a
polytope. Both random walks are sampling algorithms derived from interior point
methods. The former is based on volumetric-logarithmic barrier introduced by
Vaidya whereas the latter uses John's ellipsoids. We show that the Vaidya walk
mixes in significantly fewer steps than the logarithmic-barrier based Dikin
walk studied in past work. For a polytope in $\mathbb{R}^d$ defined by $n >d$
linear constraints, we show that the mixing time from a warm start is bounded
as $\mathcal{O}(n^{0.5}d^{1.5})$, compared to the $\mathcal{O}(nd)$ mixing time
bound for the Dikin walk. The cost of each step of the Vaidya walk is of the
same order as the Dikin walk, and at most twice as large in terms of constant
pre-factors. For the John walk, we prove an
$\mathcal{O}(d^{2.5}\cdot\log^4(n/d))$ bound on its mixing time and conjecture
that an improved variant of it could achieve a mixing time of
$\mathcal{O}(d^2\cdot\text{polylog}(n/d))$. Additionally, we propose variants
of the Vaidya and John walks that mix in polynomial time from a deterministic
starting point. The speed-up of the Vaidya walk over the Dikin walk are
illustrated in numerical examples.