pwc-archive/papers-with-abstracts huggingface dataset:
Extended Affinity Propagation: Global Discovery and Local Insights
Authors: Rayyan Ahmad Khan, Rana Ali Amjad, Martin Kleinsteuber
Date: 2018-03-12 00:00:00
Tasks: Clustering

We propose a new clustering algorithm, Extended Affinity Propagation, based
on pairwise similarities. Extended Affinity Propagation is developed by
modifying Affinity Propagation such that the desirable features of Affinity
Propagation, e.g., exemplars, reasonable computational complexity and no need
to specify number of clusters, are preserved while the shortcomings, e.g., the
lack of global structure discovery, that limit the applicability of Affinity
Propagation are overcome. Extended Affinity Propagation succeeds not only in
achieving this goal but can also provide various additional insights into the
internal structure of the individual clusters, e.g., refined confidence values,
relative cluster densities and local cluster strength in different regions of a
cluster, which are valuable for an analyst. We briefly discuss how these
insights can help in easily tuning the hyperparameters. We also illustrate
these desirable features and the performance of Extended Affinity Propagation
on various synthetic and real world datasets.
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Authors: Si-Qi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, Kevin Murphy
Date: 2016-12-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Captioning

Current image captioning methods are usually trained via (penalized) maximum
likelihood estimation. However, the log-likelihood score of a caption does not
correlate well with human assessments of quality. Standard syntactic evaluation
metrics, such as BLEU, METEOR and ROUGE, are also not well correlated. The
newer SPICE and CIDEr metrics are better correlated, but have traditionally
been hard to optimize for. In this paper, we show how to use a policy gradient
(PG) method to directly optimize a linear combination of SPICE and CIDEr (a
combination we call SPIDEr): the SPICE score ensures our captions are
semantically faithful to the image, while CIDEr score ensures our captions are
syntactically fluent. The PG method we propose improves on the prior MIXER
approach, by using Monte Carlo rollouts instead of mixing MLE training with PG.
We show empirically that our algorithm leads to easier optimization and
improved results compared to MIXER. Finally, we show that using our PG method
we can optimize any of the metrics, including the proposed SPIDEr metric which
results in image captions that are strongly preferred by human raters compared
to captions generated by the same model but trained to optimize MLE or the COCO
metrics.
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
Authors: Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
Date: 2018-03-12 00:00:00
Tasks: parameter estimation, Vocal Bursts Intensity Prediction

Linear mixed models (LMMs) are used extensively to model dependecies of
observations in linear regression and are used extensively in many application
areas. Parameter estimation for LMMs can be computationally prohibitive on big
data. State-of-the-art learning algorithms require computational complexity
which depends at least linearly on the dimension $p$ of the covariates, and
often use heuristics that do not offer theoretical guarantees. We present
scalable algorithms for learning high-dimensional LMMs with sublinear
computational complexity dependence on $p$. Key to our approach are novel dual
estimators which use only kernel functions of the data, and fast computational
techniques based on the subsampled randomized Hadamard transform. We provide
theoretical guarantees for our learning algorithms, demonstrating the
robustness of parameter estimation. Finally, we complement the theory with
experiments on large synthetic and real data.
A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar
Authors: Carlos Pena-Caballero, Elifaleth Cantu, Jesus Rodriguez, Adolfo Gonzales, Osvaldo Castellanos, Angel Cantu, Megan Strait, Jae Son, Dongchul Kim
Date: 2017-11-14 00:00:00

Along with the improvement of radar technologies, Automatic Target
Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR)
has come to be an active research area. SAR/ISAR are radar techniques to
generate a two-dimensional high-resolution image of a target. Unlike other
similar experiments using Convolutional Neural Networks (CNN) to solve this
problem, we utilize an unusual approach that leads to better performance and
faster training times. Our CNN uses complex values generated by a simulation to
train the network; additionally, we utilize a multi-radar approach to increase
the accuracy of the training and testing processes, thus resulting in higher
accuracies than the other papers working on SAR/ISAR ATR. We generated our
dataset with 7 different aircraft models with a radar simulator we developed
called RadarPixel; it is a Windows GUI program implemented using Matlab and
Java programming, the simulator is capable of accurately replicating a real
SAR/ISAR configurations. Our objective is to utilize our multi-radar technique
and determine the optimal number of radars needed to detect and classify
targets.
A Feature-Rich Vietnamese Named-Entity Recognition Model
Authors: Pham Quang Nhat Minh
Date: 2018-03-12 00:00:00
Tasks: Chunking, model, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, POS, POS Tagging, Segmentation

In this paper, we present a feature-based named-entity recognition (NER)
model that achieves the start-of-the-art accuracy for Vietnamese language. We
combine word, word-shape features, PoS, chunk, Brown-cluster-based features,
and word-embedding-based features in the Conditional Random Fields (CRF) model.
We also explore the effects of word segmentation, PoS tagging, and chunking
results of many popular Vietnamese NLP toolkits on the accuracy of the proposed
feature-based NER model. Up to now, our work is the first work that
systematically performs an extrinsic evaluation of basic Vietnamese NLP
toolkits on the downstream NER task. Experimental results show that while
automatically-generated word segmentation is useful, PoS and chunking
information generated by Vietnamese NLP tools does not show their benefits for
the proposed feature-based NER model.
Affine Differential Invariants for Invariant Feature Point Detection
Authors: Stanley L. Tuznik, Peter J. Olver, Allen Tannenbaum
Date: 2018-03-05 00:00:00

Image feature points are detected as pixels which locally maximize a detector
function, two commonly used examples of which are the (Euclidean) image
gradient and the Harris-Stephens corner detector. A major limitation of these
feature detectors are that they are only Euclidean-invariant. In this work we
demonstrate the application of a 2D affine-invariant image feature point
detector based on differential invariants as derived through the equivariant
method of moving frames. The fundamental equi-affine differential invariants
for 3D image volumes are also computed.
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Authors: Junhong Lin, Volkan Cevher
Date: 2018-03-12 00:00:00
Proceeding: ICML 2018 7
Tasks: regression

We investigate regularized algorithms combining with projection for
least-squares regression problem over a Hilbert space, covering nonparametric
regression over a reproducing kernel Hilbert space. We prove convergence
results with respect to variants of norms, under a capacity assumption on the
hypothesis space and a regularity condition on the target function. As a
result, we obtain optimal rates for regularized algorithms with randomized
sketches, provided that the sketch dimension is proportional to the effective
dimension up to a logarithmic factor. As a byproduct, we obtain similar results
for Nystr\"{o}m regularized algorithms. Our results are the first ones with
optimal, distribution-dependent rates that do not have any saturation effect
for sketched/Nystr\"{o}m regularized algorithms, considering both the
attainable and non-attainable cases.
Beyond Gröbner Bases: Basis Selection for Minimal Solvers
Authors: Viktor Larsson, Magnus Oskarsson, Kalle Åström, Alge Wallis, Zuzana Kukelova, Tomas Pajdla
Date: 2018-03-12 00:00:00

Many computer vision applications require robust estimation of the underlying
geometry, in terms of camera motion and 3D structure of the scene. These robust
methods often rely on running minimal solvers in a RANSAC framework. In this
paper we show how we can make polynomial solvers based on the action matrix
method faster, by careful selection of the monomial bases. These monomial bases
have traditionally been based on a Gr\"obner basis for the polynomial ideal.
Here we describe how we can enumerate all such bases in an efficient way. We
also show that going beyond Gr\"obner bases leads to more efficient solvers in
many cases. We present a novel basis sampling scheme that we evaluate on a
number of problems.
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding
Authors: Meng Wang
Date: 2017-12-24 00:00:00
Tasks: Graph Embedding, Knowledge Graphs, Link Prediction

Minimizing adverse reactions caused by drug-drug interactions has always been
a momentous research topic in clinical pharmacology. Detecting all possible
interactions through clinical studies before a drug is released to the market
is a demanding task. The power of big data is opening up new approaches to
discover various drug-drug interactions. However, these discoveries contain a
huge amount of noise and provide knowledge bases far from complete and
trustworthy ones to be utilized. Most existing studies focus on predicting
binary drug-drug interactions between drug pairs but ignore other interactions.
In this paper, we propose a novel framework, called PRD, to predict drug-drug
interactions. The framework uses the graph embedding that can overcome data
incompleteness and sparsity issues to achieve multiple DDI label prediction.
First, a large-scale drug knowledge graph is generated from different sources.
Then, the knowledge graph is embedded with comprehensive biomedical text into a
common low dimensional space. Finally, the learned embeddings are used to
efficiently compute rich DDI information through a link prediction process. To
validate the effectiveness of the proposed framework, extensive experiments
were conducted on real-world datasets. The results demonstrate that our model
outperforms several state-of-the-art baseline methods in terms of capability
and accuracy.
idtracker.ai: Tracking all individuals in large collectives of unmarked animals
Authors: Francisco Romero-Ferrero, Mattia G. Bergomi, Robert Hinz, Francisco J. H. Heras, Gonzalo G. de Polavieja
Date: 2018-03-12 00:00:00
Tasks: All, Multi-Animal Tracking with identification

Our understanding of collective animal behavior is limited by our ability to
track each of the individuals. We describe an algorithm and software,
idtracker.ai, that extracts from video all trajectories with correct identities
at a high accuracy for collectives of up to 100 individuals. It uses two deep
networks, one detecting when animals touch or cross and another one for animal
identification, trained adaptively to conditions and difficulty of the video.
Group Affect Prediction Using Multimodal Distributions
Authors: Saqib Shamsi, Bhanu Pratap Singh Rawat, Manya Wadhwa
Date: 2017-09-17 00:00:00
Tasks: Emotion Recognition, Prediction

We describe our approach towards building an efficient predictive model to
detect emotions for a group of people in an image. We have proposed that
training a Convolutional Neural Network (CNN) model on the emotion heatmaps
extracted from the image, outperforms a CNN model trained entirely on the raw
images. The comparison of the models have been done on a recently published
dataset of Emotion Recognition in the Wild (EmotiW) challenge, 2017. The
proposed method achieved validation accuracy of 55.23% which is 2.44% above the
baseline accuracy, provided by the EmotiW organizers.
Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings
Authors: Charles F Jekel, Raphael T. Haftka
Date: 2018-03-12 00:00:00
Tasks: General Classification

A method to produce personalized classification models to automatically
review online dating profiles on Tinder is proposed, based on the user's
historical preference. The method takes advantage of a FaceNet facial
classification model to extract features which may be related to facial
attractiveness. The embeddings from a FaceNet model were used as the features
to describe an individual's face. A user reviewed 8,545 online dating profiles.
For each reviewed online dating profile, a feature set was constructed from the
profile images which contained just one face. Two approaches are presented to
go from the set of features for each face, to a set of profile features. A
simple logistic regression trained on the embeddings from just 20 profiles
could obtain a 65% validation accuracy. A point of diminishing marginal returns
was identified to occur around 80 profiles, at which the model accuracy of 73%
would only improve marginally after reviewing a significant number of
additional profiles.
Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention
Authors: Fabiano Ferreira Luz, Marcelo Finger
Date: 2018-03-12 00:00:00
Tasks: Semantic Parsing, Sentence

Semantic parsing is the process of mapping a natural language sentence into a
formal representation of its meaning. In this work we use the neural network
approach to transform natural language sentence into a query to an ontology
database in the SPARQL language. This method does not rely on handcraft-rules,
high-quality lexicons, manually-built templates or other handmade complex
structures. Our approach is based on vector space model and neural networks.
The proposed model is based in two learning steps. The first step generates a
vector representation for the sentence in natural language and SPARQL query.
The second step uses this vector representation as input to a neural network
(LSTM with attention mechanism) to generate a model able to encode natural
language and decode SPARQL.
New Ideas for Brain Modelling 4
Authors: Kieran Greer
Date: 2017-08-16 00:00:00

This paper continues the research that considers a new cognitive model based
strongly on the human brain. In particular, it considers the neural binding
structure of an earlier paper. It also describes some new methods in the areas
of image processing and behaviour simulation. The work is all based on earlier
research by the author and the new additions are intended to fit in with the
overall design. For image processing, a grid-like structure is used with 'full
linking'. Each cell in the classifier grid stores a list of all other cells it
gets associated with and this is used as the learned image that new input is
compared to. For the behaviour metric, a new prediction equation is suggested,
as part of a simulation, that uses feedback and history to dynamically
determine its course of action. While the new methods are from widely different
topics, both can be compared with the binary-analog type of interface that is
the main focus of the paper. It is suggested that the simplest of linking
between a tree and ensemble can explain neural binding and variable signal
strengths.
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Authors: Debang Li, Huikai Wu, Junge Zhang, Kaiqi Huang
Date: 2017-09-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decision Making, Image Cropping, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. Moreover, the sliding window method usually produces tens of
thousands of windows on the input image which is very time-consuming. Motivated
by these challenges, we firstly formulate the aesthetic image cropping as a
sequential decision-making process and propose a weakly supervised Aesthetics
Aware Reinforcement Learning (A2-RL) framework to address this problem.
Particularly, the proposed method develops an aesthetics aware reward function
which especially benefits image cropping. Similar to human's decision making,
we use a comprehensive state representation including both the current
observation and the historical experience. We train the agent using the
actor-critic architecture in an end-to-end manner. The agent is evaluated on
several popular unseen cropping datasets. Experiment results show that our
method achieves the state-of-the-art performance with much fewer candidate
windows and much less time compared with previous weakly supervised methods.
Extreme Dimension Reduction for Handling Covariate Shift
Authors: Fulton Wang, Cynthia Rudin
Date: 2017-11-29 00:00:00
Tasks: Dimensionality Reduction

In the covariate shift learning scenario, the training and test covariate
distributions differ, so that a predictor's average loss over the training and
test distributions also differ. In this work, we explore the potential of
extreme dimension reduction, i.e. to very low dimensions, in improving the
performance of importance weighting methods for handling covariate shift, which
fail in high dimensions due to potentially high train/test covariate divergence
and the inability to accurately estimate the requisite density ratios. We first
formulate and solve a problem optimizing over linear subspaces a combination of
their predictive utility and train/test divergence within. Applying it to
simulated and real data, we show extreme dimension reduction helps sometimes
but not always, due to a bias introduced by dimension reduction.
Deep Learning in Mobile and Wireless Networking: A Survey
Authors: Chaoyun Zhang, Paul Patras, Hamed Haddadi
Date: 2018-03-12 00:00:00
Tasks: Deep Learning, Link Prediction, Management, Survey

The rapid uptake of mobile devices and the rising popularity of mobile
applications and services pose unprecedented demands on mobile and wireless
networking infrastructure. Upcoming 5G systems are evolving to support
exploding mobile traffic volumes, agile management of network resource to
maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly
complex, heterogeneous, and evolving. One potential solution is to resort to
advanced machine learning techniques to help managing the rise in data volumes
and algorithm-driven applications. The recent success of deep learning
underpins new and powerful tools that tackle problems in this space.
  In this paper we bridge the gap between deep learning and mobile and wireless
networking research, by presenting a comprehensive survey of the crossovers
between the two areas. We first briefly introduce essential background and
state-of-the-art in deep learning techniques with potential applications to
networking. We then discuss several techniques and platforms that facilitate
the efficient deployment of deep learning onto mobile systems. Subsequently, we
provide an encyclopedic review of mobile and wireless networking research based
on deep learning, which we categorize by different domains. Drawing from our
experience, we discuss how to tailor deep learning to mobile environments. We
complete this survey by pinpointing current challenges and open future
directions for research.
The Everlasting Database: Statistical Validity at a Fair Price
Authors: Blake Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro
Date: 2018-03-12 00:00:00
Proceeding: NeurIPS 2018 12

The problem of handling adaptivity in data analysis, intentional or not,
permeates a variety of fields, including test-set overfitting in ML challenges
and the accumulation of invalid scientific discoveries. We propose a mechanism
for answering an arbitrarily long sequence of potentially adaptive statistical
queries, by charging a price for each query and using the proceeds to collect
additional samples. Crucially, we guarantee statistical validity without any
assumptions on how the queries are generated. We also ensure with high
probability that the cost for $M$ non-adaptive queries is $O(\log M)$, while
the cost to a potentially adaptive user who makes $M$ queries that do not
depend on any others is $O(\sqrt{M})$.
Representation Learning and Recovery in the ReLU Model
Authors: Arya Mazumdar, Ankit Singh Rawat
Date: 2018-03-12 00:00:00
Tasks: Dictionary Learning, Representation Learning

Rectified linear units, or ReLUs, have become the preferred activation
function for artificial neural networks. In this paper we consider two basic
learning problems assuming that the underlying data follow a generative model
based on a ReLU-network -- a neural network with ReLU activations. As a
primarily theoretical study, we limit ourselves to a single-layer network. The
first problem we study corresponds to dictionary-learning in the presence of
nonlinearity (modeled by the ReLU functions). Given a set of observation
vectors $\mathbf{y}^i \in \mathbb{R}^d, i =1, 2, \dots , n$, we aim to recover
$d\times k$ matrix $A$ and the latent vectors $\{\mathbf{c}^i\} \subset
\mathbb{R}^k$ under the model $\mathbf{y}^i = \mathrm{ReLU}(A\mathbf{c}^i
+\mathbf{b})$, where $\mathbf{b}\in \mathbb{R}^d$ is a random bias. We show
that it is possible to recover the column space of $A$ within an error of
$O(d)$ (in Frobenius norm) under certain conditions on the probability
distribution of $\mathbf{b}$.
  The second problem we consider is that of robust recovery of the signal in
the presence of outliers, i.e., large but sparse noise. In this setting we are
interested in recovering the latent vector $\mathbf{c}$ from its noisy
nonlinear sketches of the form $\mathbf{v} = \mathrm{ReLU}(A\mathbf{c}) +
\mathbf{e}+\mathbf{w}$, where $\mathbf{e} \in \mathbb{R}^d$ denotes the
outliers with sparsity $s$ and $\mathbf{w} \in \mathbb{R}^d$ denote the dense
but small noise. This line of work has recently been studied (Soltanolkotabi,
2017) without the presence of outliers. For this problem, we show that a
generalized LASSO algorithm is able to recover the signal $\mathbf{c} \in
\mathbb{R}^k$ within an $\ell_2$ error of $O(\sqrt{\frac{(k+s)\log d}{d}})$
when $A$ is a random Gaussian matrix.
Learning unknown ODE models with Gaussian processes
Authors: Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki
Date: 2018-03-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Gaussian Processes

In conventional ODE modelling coefficients of an equation driving the system
state forward in time are estimated. However, for many complex systems it is
practically impossible to determine the equations or interactions governing the
underlying dynamics. In these settings, parametric ODE model cannot be
formulated. Here, we overcome this issue by introducing a novel paradigm of
nonparametric ODE modelling that can learn the underlying dynamics of arbitrary
continuous-time systems without prior knowledge. We propose to learn
non-linear, unknown differential functions from state observations using
Gaussian process vector fields within the exact ODE formalism. We demonstrate
the model's capabilities to infer dynamics from sparse data and to simulate the
system forward into future.