pwc-archive/papers-with-abstracts huggingface dataset:
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Authors: Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
Date: 2018-05-29 00:00:00
Tasks: General Classification, Variational Inference

In this paper, we introduce a new form of amortized variational inference by
using the forward KL divergence in a joint-contrastive variational loss. The
resulting forward amortized variational inference is a likelihood-free method
as its gradient can be sampled without bias and without requiring any
evaluation of either the model joint distribution or its derivatives. We prove
that our new variational loss is optimized by the exact posterior marginals in
the fully factorized mean-field approximation, a property that is not shared
with the more conventional reverse KL inference. Furthermore, we show that
forward amortized inference can be easily marginalized over large families of
latent variables in order to obtain a marginalized variational posterior. We
consider two examples of variational marginalization. In our first example we
train a Bayesian forecaster for predicting a simplified chaotic model of
atmospheric convection. In the second example we train an amortized variational
approximation of a Bayesian optimal classifier by marginalizing over the model
space. The result is a powerful meta-classification network that can solve
arbitrary classification problems without further training.
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling
Authors: Kun Yuan, Bicheng Ying, Jiageng Liu, Ali H. Sayed
Date: 2017-08-04 00:00:00

A new amortized variance-reduced gradient (AVRG) algorithm was developed in
\cite{ying2017convergence}, which has constant storage requirement in
comparison to SAGA and balanced gradient computations in comparison to SVRG.
One key advantage of the AVRG strategy is its amenability to decentralized
implementations. In this work, we show how AVRG can be extended to the network
case where multiple learning agents are assumed to be connected by a graph
topology. In this scenario, each agent observes data that is spatially
distributed and all agents are only allowed to communicate with direct
neighbors. Moreover, the amount of data observed by the individual agents may
differ drastically. For such situations, the balanced gradient computation
property of AVRG becomes a real advantage in reducing idle time caused by
unbalanced local data storage requirements, which is characteristic of other
reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is
shown to have linear convergence to the exact solution, and is much more memory
efficient than other alternative algorithms. In addition, we propose a
mini-batch strategy to balance the communication and computation efficiency for
diffusion-AVRG. When a proper batch size is employed, it is observed in
simulations that diffusion-AVRG is more computationally efficient than exact
diffusion or EXTRA while maintaining almost the same communication efficiency.
Combinets: Creativity via Recombination of Neural Networks
Authors: Matthew Guzdial, Mark O. Riedl
Date: 2018-02-10 00:00:00
Tasks: General Classification, image-classification, Image Classification, Image Generation, Transfer Learning

One of the defining characteristics of human creativity is the ability to
make conceptual leaps, creating something surprising from typical knowledge. In
comparison, deep neural networks often struggle to handle cases outside of
their training data, which is especially problematic for problems with limited
training data. Approaches exist to transfer knowledge from problems with
sufficient data to those with insufficient data, but they tend to require
additional training or a domain-specific method of transfer. We present a new
approach, conceptual expansion, that serves as a general representation for
reusing existing trained models to derive new models without backpropagation.
We evaluate our approach on few-shot variations of two tasks: image
classification and image generation, and outperform standard transfer learning
approaches.
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Authors: Yi Tay, Anh Tuan Luu, Siu Cheung Hui
Date: 2018-05-29 00:00:00
Tasks: Deep Learning, Recommendation Systems

Dating and romantic relationships not only play a huge role in our personal
lives but also collectively influence and shape society. Today, many romantic
partnerships originate from the Internet, signifying the importance of
technology and the web in modern dating. In this paper, we present a text-based
computational approach for estimating the relationship compatibility of two
users on social media. Unlike many previous works that propose reciprocal
recommender systems for online dating websites, we devise a distant supervision
heuristic to obtain real world couples from social platforms such as Twitter.
Our approach, the CoupleNet is an end-to-end deep learning based estimator that
analyzes the social profiles of two users and subsequently performs a
similarity match between the users. Intuitively, our approach performs both
user profiling and match-making within a unified end-to-end framework.
CoupleNet utilizes hierarchical recurrent neural models for learning
representations of user profiles and subsequently coupled attention mechanisms
to fuse information aggregated from two users. To the best of our knowledge,
our approach is the first data-driven deep learning approach for our novel
relationship recommendation problem. We benchmark our CoupleNet against several
machine learning and deep learning baselines. Experimental results show that
our approach outperforms all approaches significantly in terms of precision.
Qualitative analysis shows that our model is capable of also producing
explainable results to users.
airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution
Authors: M. Benjamin Sabath, Qian Di, Danielle Braun, Joel Schwarz, Francesca Dominici, Christine Choirat
Date: 2018-05-29 00:00:00

Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants
regulated by the Environmental Protection Agency in the United States. There is
strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of
mortality and hospitalization. Large scale epidemiological studies on the
health effects of PM$_{2.5}$ provide the necessary evidence base for lowering
the safety standards and inform regulatory policy. However, ambient monitors of
PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located
across the U.S., and therefore studies based only on the levels of PM$_{2.5}$
measured from the monitors would inevitably exclude large amounts of the
population. One approach to resolving this issue has been developing models to
predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological,
and land use data. This process typically relies developing a prediction model
that relies on large amounts of input data and is highly computationally
intensive to predict levels of air pollution in unmonitored areas. We have
developed a flexible R package that allows for environmental health researchers
to design and train spatio-temporal models capable of predicting multiple
pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data
platform, to achieve both performance and scalability when used in conjunction
with cloud or cluster computing systems.
Joint Spatial-Angular Sparse Coding for dMRI with Separable Dictionaries
Authors: Evan Schwab, René Vidal, Nicolas Charon
Date: 2016-12-18 00:00:00
Tasks: compressed sensing, Diffusion  MRI

Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in
the brain, $\textit{in vivo}$, by measuring water diffusion along angular
gradient directions in q-space. High angular resolution diffusion imaging
(HARDI) can produce better estimates of fiber orientation than the popularly
used diffusion tensor imaging, but the high number of samples needed to
estimate diffusivity requires longer patient scan times. To accelerate dMRI,
compressed sensing (CS) has been utilized by exploiting a sparse dictionary
representation of the data, discovered through sparse coding. The sparser the
representation, the fewer samples are needed to reconstruct a high resolution
signal with limited information loss, and so an important area of research has
focused on finding the sparsest possible representation of dMRI. Current
reconstruction methods however, rely on an angular representation $\textit{per
voxel}$ with added spatial regularization, and so, for non-zero signals, one is
required to have at least one non-zero coefficient per voxel. This means that
the global level of sparsity must be greater than the number of voxels. In
contrast, we propose a joint spatial-angular representation of dMRI that will
allow us to achieve levels of global sparsity that are below the number of
voxels. A major challenge, however, is the computational complexity of solving
a global sparse coding problem over large-scale dMRI. In this work, we present
novel adaptations of popular sparse coding algorithms that become better suited
for solving large-scale problems by exploiting spatial-angular separability.
Our experiments show that our method achieves significantly sparser
representations of HARDI than is possible by the state of the art.
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques
Authors: Pei Li, Loreto Prieto, Domingo Mery, Patrick Flynn
Date: 2018-05-29 00:00:00
Tasks: Face Identification, Face Recognition, Super-Resolution

Although face recognition systems have achieved impressive performance in
recent years, the low-resolution face recognition (LRFR) task remains
challenging, especially when the LR faces are captured under non-ideal
conditions, as is common in surveillance-based applications. Faces captured in
such conditions are often contaminated by blur, nonuniform lighting, and
nonfrontal face pose. In this paper, we analyze face recognition techniques
using data captured under low-quality conditions in the wild. We provide a
comprehensive analysis of experimental results for two of the most important
applications in real surveillance applications, and demonstrate practical
approaches to handle both cases that show promising performance. The following
three contributions are made: {\em (i)} we conduct experiments to evaluate
super-resolution methods for low-resolution face recognition; {\em (ii)} we
study face re-identification on various public face datasets including real
surveillance and low-resolution subsets of large-scale datasets, present a
baseline result for several deep learning based approaches, and improve them by
introducing a GAN pre-training approach and fully convolutional architecture;
and {\em (iii)} we explore low-resolution face identification by employing a
state-of-the-art supervised discriminative learning approach. Evaluations are
conducted on challenging portions of the SCFace and UCCSface datasets.
Learning to Transcribe by Ear
Authors: Rainer Kelz, Gerhard Widmer
Date: 2018-05-29 00:00:00
Tasks: Reinforcement Learning

Rethinking how to model polyphonic transcription formally, we frame it as a
reinforcement learning task. Such a task formulation encompasses the notion of
a musical agent and an environment containing an instrument as well as the
sound source to be transcribed. Within this conceptual framework, the
transcription process can be described as the agent interacting with the
instrument in the environment, and obtaining reward by playing along with what
it hears. Choosing from a discrete set of actions - the notes to play on its
instrument - the amount of reward the agent experiences depends on which notes
it plays and when. This process resembles how a human musician might approach
the task of transcription, and the satisfaction she achieves by closely
mimicking the sound source to transcribe on her instrument. Following a
discussion of the theoretical framework and the benefits of modelling the
problem in this way, we focus our attention on several practical considerations
and address the difficulties in training an agent to acceptable performance on
a set of tasks with increasing difficulty. We demonstrate promising results in
partially constrained environments.
Face Recognition in Low Quality Images: A Survey
Authors: Pei Li, Loreto Prieto, Domingo Mery, Patrick Flynn
Date: 2018-05-29 00:00:00
Tasks: Deblurring, Face Recognition, Super-Resolution, Survey

Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.
Multi-Reward Reinforced Summarization with Saliency and Entailment
Authors: Ramakanth Pasunuru, Mohit Bansal
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstractive Text Summarization, Reinforcement Learning, Text Summarization

Abstractive text summarization is the task of compressing and rewriting a
long document into a short summary while maintaining saliency, directed logical
entailment, and non-redundancy. In this work, we address these three important
aspects of a good summary via a reinforcement learning approach with two novel
reward functions: ROUGESal and Entail, on top of a coverage-based baseline. The
ROUGESal reward modifies the ROUGE metric by up-weighting the salient
phrases/words detected via a keyphrase classifier. The Entail reward gives high
(length-normalized) scores to logically-entailed summaries using an entailment
classifier. Further, we show superior performance improvement when these
rewards are combined with traditional metric (ROUGE) based rewards, via our
novel and effective multi-reward approach of optimizing multiple rewards
simultaneously in alternate mini-batches. Our method achieves the new
state-of-the-art results (including human evaluation) on the CNN/Daily Mail
dataset as well as strong improvements in a test-only transfer setup on
DUC-2002.
Hyperparameter Importance Across Datasets
Authors: J. N. van Rijn, F. Hutter
Date: 2017-10-12 00:00:00
Tasks: Hyperparameter Optimization, Meta-Learning

With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.
Classification with imperfect training labels
Authors: Timothy I. Cannings, Yingying Fan, Richard J. Samworth
Date: 2018-05-29 00:00:00
Tasks: Classification, General Classification

We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour ($k$nn), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. One consequence of these results is that the knn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. On the other hand, the LDA classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of each class are equal. Our theoretical results are supported by a simulation study.
Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks
Authors: Umair Javaid, John A. Lee
Date: 2018-05-29 00:00:00
Tasks: Computed Tomography (CT), Data Augmentation

With the advent of Deep Learning (DL) techniques, especially Generative
Adversarial Networks (GANs), data augmentation and generation are quickly
evolving domains that have raised much interest recently. However, the DL
techniques are data demanding and since, medical data is not easily accessible,
they suffer from data insufficiency. To deal with this limitation, different
data augmentation techniques are used. Here, we propose a novel unsupervised
data-driven approach for data augmentation that can generate 2D Computed
Tomography (CT) images using a simple GAN. The generated CT images have good
global and local features of a real CT image and can be used to augment the
training datasets for effective learning. In this proof-of-concept study, we
show that our proposed solution using GANs is able to capture some of the
global and local CT variabilities. Our network is able to generate visually
realistic CT images and we aim to further enhance its output by scaling it to a
higher resolution and potentially from 2D to 3D.
Efficient Bayesian Inference for a Gaussian Process Density Model
Authors: Christian Donner, Manfred Opper
Date: 2018-05-29 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

We reconsider a nonparametric density model based on Gaussian processes. By
augmenting the model with latent P\'olya--Gamma random variables and a latent
marked Poisson process we obtain a new likelihood which is conjugate to the
model's Gaussian process prior. The augmented posterior allows for efficient
inference by Gibbs sampling and an approximate variational mean field approach.
For the latter we utilise sparse GP approximations to tackle the infinite
dimensionality of the problem. The performance of both algorithms and
comparisons with other density estimators are demonstrated on artificial and
real datasets with up to several thousand data points.
On consistent vertex nomination schemes
Authors: Vince Lyzinski, Keith Levin, Carey E. Priebe
Date: 2017-11-15 00:00:00
Tasks: Information Retrieval, Retrieval

Given a vertex of interest in a network $G_1$, the vertex nomination problem
seeks to find the corresponding vertex of interest (if it exists) in a second
network $G_2$. A vertex nomination scheme produces a list of the vertices in
$G_2$, ranked according to how likely they are judged to be the corresponding
vertex of interest in $G_2$. The vertex nomination problem and related
information retrieval tasks have attracted much attention in the machine
learning literature, with numerous applications to social and biological
networks. However, the current framework has often been confined to a
comparatively small class of network models, and the concept of statistically
consistent vertex nomination schemes has been only shallowly explored. In this
paper, we extend the vertex nomination problem to a very general statistical
model of graphs. Further, drawing inspiration from the long-established
classification framework in the pattern recognition literature, we provide
definitions for the key notions of Bayes optimality and consistency in our
extended vertex nomination framework, including a derivation of the Bayes
optimal vertex nomination scheme. In addition, we prove that no universally
consistent vertex nomination schemes exist. Illustrative examples are provided
throughout.
An Improved Video Analysis using Context based Extension of LSH
Authors: Angana Chakraborty, Sanghamitra Bandyopadhyay
Date: 2017-05-10 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization, Video Retrieval

Locality Sensitive Hashing (LSH) based algorithms have already shown their
promise in finding approximate nearest neighbors in high dimen- sional data
space. However, there are certain scenarios, as in sequential data, where the
proximity of a pair of points cannot be captured without considering their
surroundings or context. In videos, as for example, a particular frame is
meaningful only when it is seen in the context of its preceding and following
frames. LSH has no mechanism to handle the con- texts of the data points. In
this article, a novel scheme of Context based Locality Sensitive Hashing
(conLSH) has been introduced, in which points are hashed together not only
based on their closeness, but also because of similar context. The contribution
made in this article is three fold. First, conLSH is integrated with a recently
proposed fast optimal sequence alignment algorithm (FOGSAA) using a layered
approach. The resultant method is applied to video retrieval for extracting
similar sequences. The pro- posed algorithm yields more than 80% accuracy on an
average in different datasets. It has been found to save 36.3% of the total
time, consumed by the exhaustive search. conLSH reduces the search space to
approximately 42% of the entire dataset, when compared with an exhaustive
search by the aforementioned FOGSAA, Bag of Words method and the standard LSH
implementations. Secondly, the effectiveness of conLSH is demon- strated in
action recognition of the video clips, which yields an average gain of 12.83%
in terms of classification accuracy over the state of the art methods using
STIP descriptors. The last but of great significance is that this article
provides a way of automatically annotating long and composite real life videos.
The source code of conLSH is made available at
http://www.isical.ac.in/~bioinfo_miu/conLSH/conLSH.html