pwc-archive/papers-with-abstracts huggingface dataset:
Stable Distribution Alignment Using the Dual of the Adversarial Distance
Authors: Ben Usman, Kate Saenko, Brian Kulis
Date: 2017-07-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Domain Adaptation

Methods that align distributions by minimizing an adversarial distance
between them have recently achieved impressive results. However, these
approaches are difficult to optimize with gradient descent and they often do
not converge well without careful hyperparameter tuning and proper
initialization. We investigate whether turning the adversarial min-max problem
into an optimization problem by replacing the maximization part with its dual
improves the quality of the resulting alignment and explore its connections to
Maximum Mean Discrepancy. Our empirical results suggest that using the dual
formulation for the restricted family of linear discriminators results in a
more stable convergence to a desirable solution when compared with the
performance of a primal min-max GAN-like objective and an MMD objective under
the same restrictions. We test our hypothesis on the problem of aligning two
synthetic point clouds on a plane and on a real-image domain adaptation problem
on digits. In both cases, the dual formulation yields an iterative procedure
that gives more stable and monotonic improvement over time.
A novel methodology on distributed representations of proteins using their interacting ligands
Authors: Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
Date: 2018-01-30 00:00:00
Tasks: Clustering, Word Embeddings

The effective representation of proteins is a crucial task that directly
affects the performance of many bioinformatics problems. Related proteins
usually bind to similar ligands. Chemical characteristics of ligands are known
to capture the functional and mechanistic properties of proteins suggesting
that a ligand based approach can be utilized in protein representation. In this
study, we propose SMILESVec, a SMILES-based method to represent ligands and a
novel method to compute similarity of proteins by describing them based on
their ligands. The proteins are defined utilizing the word-embeddings of the
SMILES strings of their ligands. The performance of the proposed protein
description method is evaluated in protein clustering task using TransClust and
MCL algorithms. Two other protein representation methods that utilize protein
sequence, BLAST and ProtVec, and two compound fingerprint based protein
representation methods are compared. We showed that ligand-based protein
representation, which uses only SMILES strings of the ligands that proteins
bind to, performs as well as protein-sequence based representation methods in
protein clustering. The results suggest that ligand-based protein description
can be an alternative to the traditional sequence or structure based
representation of proteins and this novel approach can be applied to different
bioinformatics problems such as prediction of new protein-ligand interactions
and protein function annotation.
Generating Wikipedia by Summarizing Long Sequences
Authors: Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
Date: 2018-01-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Articles, Decoder, Document Summarization, Extractive Summarization, Multi-Document Summarization, Sentence

We show that generating English Wikipedia articles can be approached as a
multi- document summarization of source documents. We use extractive
summarization to coarsely identify salient information and a neural abstractive
model to generate the article. For the abstractive model, we introduce a
decoder-only architecture that can scalably attend to very long sequences, much
longer than typical encoder- decoder architectures used in sequence
transduction. We show that this model can generate fluent, coherent
multi-sentence paragraphs and even whole Wikipedia articles. When given
reference documents, we show it can extract relevant factual information as
reflected in perplexity, ROUGE scores and human evaluations.
How linguistic descriptions of data can help to the teaching-learning process in higher education, case of study: artificial intelligence
Authors: Clemente Rubio-Manzano, Tomas Lermanda Senoceain
Date: 2017-11-27 00:00:00

Artificial Intelligence is a central topic in the computer science
curriculum. From the year 2011 a project-based learning methodology based on
computer games has been designed and implemented into the intelligence
artificial course at the University of the Bio-Bio. The project aims to develop
software-controlled agents (bots) which are programmed by using heuristic
algorithms seen during the course. This methodology allows us to obtain good
learning results, however several challenges have been founded during its
implementation.
  In this paper we show how linguistic descriptions of data can help to provide
students and teachers with technical and personalized feedback about the
learned algorithms. Algorithm behavior profile and a new Turing test for
computer games bots based on linguistic modelling of complex phenomena are also
proposed in order to deal with such challenges.
  In order to show and explore the possibilities of this new technology, a web
platform has been designed and implemented by one of authors and its
incorporation in the process of assessment allows us to improve the teaching
learning process.
Enhancing SDO/HMI images using deep learning
Authors: C. J. Diaz Baso, A. Asensio Ramos
Date: 2017-06-09 00:00:00
Tasks: Deep Learning

The Helioseismic and Magnetic Imager (HMI) provides continuum images and
magnetograms with a cadence better than one per minute. It has been
continuously observing the Sun 24 hours a day for the past 7 years. The obvious
trade-off between full disk observations and spatial resolution makes HMI not
enough to analyze the smallest-scale events in the solar atmosphere. Our aim is
to develop a new method to enhance HMI data, simultaneously deconvolving and
super-resolving images and magnetograms. The resulting images will mimic
observations with a diffraction-limited telescope twice the diameter of HMI.
Our method, which we call Enhance, is based on two deep fully convolutional
neural networks that input patches of HMI observations and output deconvolved
and super-resolved data. The neural networks are trained on synthetic data
obtained from simulations of the emergence of solar active regions. We have
obtained deconvolved and supper-resolved HMI images. To solve this ill-defined
problem with infinite solutions we have used a neural network approach to add
prior information from the simulations. We test Enhance against Hinode data
that has been degraded to a 28 cm diameter telescope showing very good
consistency. The code is open source.
A Rational Distributed Process-level Account of Independence Judgment
Authors: Ardavan S. Nobandegani, Ioannis N. Psaromiligkos
Date: 2018-01-30 00:00:00
Tasks: Detection of Dependencies

It is inconceivable how chaotic the world would look to humans, faced with
innumerable decisions a day to be made under uncertainty, had they been lacking
the capacity to distinguish the relevant from the irrelevant---a capacity which
computationally amounts to handling probabilistic independence relations. The
highly parallel and distributed computational machinery of the brain suggests
that a satisfying process-level account of human independence judgment should
also mimic these features. In this work, we present the first rational,
distributed, message-passing, process-level account of independence judgment,
called $\mathcal{D}^\ast$. Interestingly, $\mathcal{D}^\ast$ shows a curious,
but normatively-justified tendency for quick detection of dependencies,
whenever they hold. Furthermore, $\mathcal{D}^\ast$ outperforms all the
previously proposed algorithms in the AI literature in terms of worst-case
running time, and a salient aspect of it is supported by recent work in
neuroscience investigating possible implementations of Bayes nets at the neural
level. $\mathcal{D}^\ast$ nicely exemplifies how the pursuit of cognitive
plausibility can lead to the discovery of state-of-the-art algorithms with
appealing properties, and its simplicity makes $\mathcal{D}^\ast$ potentially a
good candidate for pedagogical purposes.
Sometimes You Want to Go Where Everybody Knows your Name
Authors: Reuben Brasher, Nat Roth, Justin Wagle
Date: 2018-01-30 00:00:00

We introduce a new metric for measuring how well a model personalizes to a
user's specific preferences. We define personalization as a weighting between
performance on user specific data and performance on a more general global
dataset that represents many different users. This global term serves as a form
of regularization that forces us to not overfit to individual users who have
small amounts of data. In order to protect user privacy, we add the constraint
that we may not centralize or share user data. We also contribute a simple
experiment in which we simulate classifying sentiment for users with very
distinct vocabularies. This experiment functions as an example of the tension
between doing well globally on all users, and doing well on any specific
individual user. It also provides a concrete example of how to employ our new
metric to help reason about and resolve this tension. We hope this work can
help frame and ground future work into personalization.
Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions
Authors: Behnaz Nojavanasghari, Yuchi Huang, Saad Khan
Date: 2018-01-27 00:00:00
Tasks: Facial expression generation

A social interaction is a social exchange between two or more
individuals,where individuals modify and adjust their behaviors in response to
their interaction partners. Our social interactions are one of most fundamental
aspects of our lives and can profoundly affect our mood, both positively and
negatively. With growing interest in virtual reality and avatar-mediated
interactions,it is desirable to make these interactions natural and human like
to promote positive effect in the interactions and applications such as
intelligent tutoring systems, automated interview systems and e-learning. In
this paper, we propose a method to generate facial behaviors for an agent.
These behaviors include facial expressions and head pose and they are generated
considering the users affective state. Our models learn semantically meaningful
representations of the face and generate appropriate and temporally smooth
facial behaviors in dyadic interactions.
Learning to Classify from Impure Samples with High-Dimensional Data
Authors: Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz
Date: 2018-01-30 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction, Weakly-supervised Learning

A persistent challenge in practical classification tasks is that labeled
training sets are not always available. In particle physics, this challenge is
surmounted by the use of simulations. These simulations accurately reproduce
most features of data, but cannot be trusted to capture all of the complex
correlations exploitable by modern machine learning methods. Recent work in
weakly supervised learning has shown that simple, low-dimensional classifiers
can be trained using only the impure mixtures present in data. Here, we
demonstrate that complex, high-dimensional classifiers can also be trained on
impure mixtures using weak supervision techniques, with performance comparable
to what could be achieved with pure samples. Using weak supervision will
therefore allow us to avoid relying exclusively on simulations for
high-dimensional classification. This work opens the door to a new regime
whereby complex models are trained directly on data, providing direct access to
probe the underlying physics.
Ensemble Adversarial Training: Attacks and Defenses
Authors: Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel
Date: 2017-05-19 00:00:00
Proceeding: ICLR 2018 1

Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss. We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step. We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with strong robustness to black-box attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks. However, subsequent work found that more elaborate black-box attacks could significantly enhance transferability and reduce the accuracy of our models.
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Authors: Changhao Chen, Xiaoxuan Lu, Andrew Markham, Niki Trigoni
Date: 2018-01-30 00:00:00
Tasks: Indoor Localization, State Estimation

Inertial sensors play a pivotal role in indoor localization, which in turn
lays the foundation for pervasive personal applications. However, low-cost
inertial sensors, as commonly found in smartphones, are plagued by bias and
noise, which leads to unbounded growth in error when accelerations are double
integrated to obtain displacement. Small errors in state estimation propagate
to make odometry virtually unusable in a matter of seconds. We propose to break
the cycle of continuous integration, and instead segment inertial data into
independent windows. The challenge becomes estimating the latent states of each
window, such as velocity and orientation, as these are not directly observable
from sensor data. We demonstrate how to formulate this as an optimization
problem, and show how deep recurrent neural networks can yield highly accurate
trajectories, outperforming state-of-the-art shallow techniques, on a wide
range of tests and attachments. In particular, we demonstrate that IONet can
generalize to estimate odometry for non-periodic motion, such as a shopping
trolley or baby-stroller, an extremely challenging task for existing
techniques.
Activation Maximization Generative Adversarial Nets
Authors: Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Wei-Nan Zhang, Yong Yu, Jun Wang
Date: 2017-03-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Diversity

Class labels have been empirically shown useful in improving the sample
quality of generative adversarial nets (GANs). In this paper, we mathematically
study the properties of the current variants of GANs that make use of class
label information. With class aware gradient and cross-entropy decomposition,
we reveal how class labels and associated losses influence GAN's training.
Based on that, we propose Activation Maximization Generative Adversarial
Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been
conducted to validate our analysis and evaluate the effectiveness of our
solution, where AM-GAN outperforms other strong baselines and achieves
state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we
demonstrate that, with the Inception ImageNet classifier, Inception Score
mainly tracks the diversity of the generator, and there is, however, no
reliable evidence that it can reflect the true sample quality. We thus propose
a new metric, called AM Score, to provide a more accurate estimation of the
sample quality. Our proposed model also outperforms the baseline methods in the
new metric.
Links: A High-Dimensional Online Clustering Method
Authors: Philip Andrew Mansfield, Quan Wang, Carlton Downey, Li Wan, Ignacio Lopez Moreno
Date: 2018-01-30 00:00:00
Tasks: Clustering, Online Clustering, Vocal Bursts Intensity Prediction

We present a novel algorithm, called Links, designed to perform online
clustering on unit vectors in a high-dimensional Euclidean space. The algorithm
is appropriate when it is necessary to cluster data efficiently as it streams
in, and is to be contrasted with traditional batch clustering algorithms that
have access to all data at once. For example, Links has been successfully
applied to embedding vectors generated from face images or voice recordings for
the purpose of recognizing people, thereby providing real-time identification
during video or audio capture.
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions
Authors: Quanzeng You, Hailin Jin, Jiebo Luo
Date: 2018-01-30 00:00:00
Tasks: Image Captioning, Natural Language Understanding

Automatic image captioning has recently approached human-level performance
due to the latest advances in computer vision and natural language
understanding. However, most of the current models can only generate plain
factual descriptions about the content of a given image. However, for human
beings, image caption writing is quite flexible and diverse, where additional
language dimensions, such as emotion, humor and language styles, are often
incorporated to produce diverse, emotional, or appealing captions. In
particular, we are interested in generating sentiment-conveying image
descriptions, which has received little attention. The main challenge is how to
effectively inject sentiments into the generated captions without altering the
semantic matching between the visual content and the generated descriptions. In
this work, we propose two different models, which employ different schemes for
injecting sentiments into image captions. Compared with the few existing
approaches, the proposed models are much simpler and yet more effective. The
experimental results show that our model outperform the state-of-the-art models
in generating sentimental (i.e., sentiment-bearing) image captions. In
addition, we can also easily manipulate the model by assigning different
sentiments to the testing image to generate captions with the corresponding
sentiments.
Video-based Sign Language Recognition without Temporal Segmentation
Authors: Jie Huang, Wengang Zhou, Qilin Zhang, Houqiang Li, Weiping Li
Date: 2018-01-30 00:00:00
Tasks: Segmentation, Sentence, Sign Language Recognition

Millions of hearing impaired people around the world routinely use some
variants of sign languages to communicate, thus the automatic translation of a
sign language is meaningful and important. Currently, there are two
sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that
recognizes word by word and continuous SLR that translates entire sentences.
Existing continuous SLR methods typically utilize isolated SLRs as building
blocks, with an extra layer of preprocessing (temporal segmentation) and
another layer of post-processing (sentence synthesis). Unfortunately, temporal
segmentation itself is non-trivial and inevitably propagates errors into
subsequent steps. Worse still, isolated SLR methods typically require strenuous
labeling of each word separately in a sentence, severely limiting the amount of
attainable training data. To address these challenges, we propose a novel
continuous sign recognition framework, the Hierarchical Attention Network with
Latent Space (LS-HAN), which eliminates the preprocessing of temporal
segmentation. The proposed LS-HAN consists of three components: a two-stream
Convolutional Neural Network (CNN) for video feature representation generation,
a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention
Network (HAN) for latent space based recognition. Experiments are carried out
on two large scale datasets. Experimental results demonstrate the effectiveness
of the proposed framework.
End-to-end Conversation Modeling Track in DSTC6
Authors: Chiori Hori, Takaaki Hori
Date: 2017-06-22 00:00:00

End-to-end training of neural networks is a promising approach to automatic
construction of dialog systems using a human-to-human dialog corpus. Recently,
Vinyals et al. tested neural conversation models using OpenSubtitles. Lowe et
al. released the Ubuntu Dialogue Corpus for researching unstructured multi-turn
dialogue systems. Furthermore, the approach has been extended to accomplish
task oriented dialogs to provide information properly with natural
conversation. For example, Ghazvininejad et al. proposed a knowledge grounded
neural conversation model [3], where the research is aiming at combining
conversational dialogs with task-oriented knowledge using unstructured data
such as Twitter data for conversation and Foursquare data for external
knowledge.However, the task is still limited to a restaurant information
service, and has not yet been tested with a wide variety of dialog tasks. In
addition, it is still unclear how to create intelligent dialog systems that can
respond like a human agent.
  In consideration of these problems, we proposed a challenge track to the 6th
dialog system technology challenges (DSTC6) using human-to-human dialog data to
mimic human dialog behaviors. The focus of the challenge track is to train
end-to-end conversation models from human-to-human conversation and accomplish
end-to-end dialog tasks in various situations assuming a customer service, in
which a system plays a role of human agent and generates natural and
informative sentences in response to user's questions or comments given dialog
context.
Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator
Authors: Nicolas Garcia Trillos, Moritz Gerlach, Matthias Hein, Dejan Slepcev
Date: 2018-01-30 00:00:00

We study the convergence of the graph Laplacian of a random geometric graph
generated by an i.i.d. sample from a $m$-dimensional submanifold $M$ in $R^d$
as the sample size $n$ increases and the neighborhood size $h$ tends to zero.
We show that eigenvalues and eigenvectors of the graph Laplacian converge with
a rate of $O\Big(\big(\frac{\log n}{n}\big)^\frac{1}{2m}\Big)$ to the
eigenvalues and eigenfunctions of the weighted Laplace-Beltrami operator of
$M$.
  No information on the submanifold $M$ is needed in the construction of the
graph or the "out-of-sample extension" of the eigenvectors. Of independent
interest is a generalization of the rate of convergence of empirical measures
on submanifolds in $R^d$ in infinity transportation distance.
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms
Authors: James Zhang, Ilija Vukotic, Robert Gardner
Date: 2018-01-30 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Fraud Detection, Intrusion Detection, Sensitivity

Anomaly detection is the practice of identifying items or events that do not
conform to an expected behavior or do not correlate with other items in a
dataset. It has previously been applied to areas such as intrusion detection,
system health monitoring, and fraud detection in credit card transactions. In
this paper, we describe a new method for detecting anomalous behavior over
network performance data, gathered by perfSONAR, using two machine learning
algorithms: Boosted Decision Trees (BDT) and Simple Feedforward Neural Network.
The effectiveness of each algorithm was evaluated and compared. Both have shown
sufficient performance and sensitivity.