pwc-archive/papers-with-abstracts huggingface dataset:
Recognizing Material Properties from Images
Authors: Gabriel Schwartz, Ko Nishino
Date: 2018-01-09 00:00:00
Tasks: Material Classification, Material Recognition, Scene Understanding, Transfer Learning

Humans rely on properties of the materials that make up objects to guide our
interactions with them. Grasping smooth materials, for example, requires care,
and softness is an ideal property for fabric used in bedding. Even when these
properties are not visual (e.g. softness is a physical property), we may still
infer their presence visually. We refer to such material properties as visual
material attributes. Recognizing these attributes in images can contribute
valuable information for general scene understanding and material recognition.
Unlike well-known object and scene attributes, visual material attributes are
local properties with no fixed shape or spatial extent. We show that given a
set of images annotated with known material attributes, we may accurately
recognize the attributes from small local image patches. Obtaining such
annotations in a consistent fashion at scale, however, is challenging. To
address this, we introduce a method that allows us to probe the human visual
perception of materials by asking simple yes/no questions comparing pairs of
image patches. This provides sufficient weak supervision to build a set of
attributes and associated classifiers that, while unnamed, serve the same
function as the named attributes we use to describe materials. Doing so allows
us to recognize visual material attributes without resorting to exhaustive
manual annotation of a fixed set of named attributes. Furthermore, we show that
this method may be integrated in the end-to-end learning of a material
classification CNN to simultaneously recognize materials and discover their
visual attributes. Our experimental results show that visual material
attributes, whether named or automatically discovered, provide a useful
intermediate representation for known material categories themselves as well as
a basis for transfer learning when recognizing previously-unseen categories.
Skyline Identification in Multi-Armed Bandits
Authors: Albert Cheu, Ravi Sundaram, Jonathan Ullman
Date: 2017-11-12 00:00:00
Tasks: Multi-Armed Bandits

We introduce a variant of the classical PAC multi-armed bandit problem. There
is an ordered set of $n$ arms $A[1],\dots,A[n]$, each with some stochastic
reward drawn from some unknown bounded distribution. The goal is to identify
the $skyline$ of the set $A$, consisting of all arms $A[i]$ such that $A[i]$
has larger expected reward than all lower-numbered arms $A[1],\dots,A[i-1]$. We
define a natural notion of an $\varepsilon$-approximate skyline and prove
matching upper and lower bounds for identifying an $\varepsilon$-skyline.
Specifically, we show that in order to identify an $\varepsilon$-skyline from
among $n$ arms with probability $1-\delta$, $$
\Theta\bigg(\frac{n}{\varepsilon^2} \cdot \min\bigg\{
\log\bigg(\frac{1}{\varepsilon \delta}\bigg), \log\bigg(\frac{n}{\delta}\bigg)
\bigg\} \bigg) $$ samples are necessary and sufficient. When $\varepsilon \gg
1/n$, our results improve over the naive algorithm, which draws enough samples
to approximate the expected reward of every arm; the algorithm of (Auer et al.,
AISTATS'16) for Pareto-optimal arm identification is likewise superseded. Our
results show that the sample complexity of the skyline problem lies strictly in
between that of best arm identification (Even-Dar et al., COLT'02) and that of
approximating the expected reward of every arm.
Spatially Transformed Adversarial Examples
Authors: Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song
Date: 2018-01-08 00:00:00
Proceeding: ICLR 2018 1

Recent studies show that widely used deep neural networks (DNNs) are
vulnerable to carefully crafted adversarial examples. Many advanced algorithms
have been proposed to generate adversarial examples by leveraging the
$\mathcal{L}_p$ distance for penalizing perturbations. Researchers have
explored different defense methods to defend against such adversarial attacks.
While the effectiveness of $\mathcal{L}_p$ distance as a metric of perceptual
quality remains an active research area, in this paper we will instead focus on
a different type of perturbation, namely spatial transformation, as opposed to
manipulating the pixel values directly as in prior works. Perturbations
generated through spatial transformation could result in large $\mathcal{L}_p$
distance measures, but our extensive experiments show that such spatially
transformed adversarial examples are perceptually realistic and more difficult
to defend against with existing defense systems. This potentially provides a
new direction in adversarial example generation and the design of corresponding
defenses. We visualize the spatial transformation based perturbation for
different examples and show that our technique can produce realistic
adversarial examples with smooth image deformation. Finally, we visualize the
attention of deep networks with different types of adversarial examples to
better understand how these examples are interpreted.
Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are Complementary to Neural Embeddings
Authors: Neil R. Smalheiser, Gary Bonifield
Date: 2018-01-05 00:00:00
Tasks: text similarity, Word Similarity

Neural embeddings are a popular set of methods for representing words,
phrases or text as a low dimensional vector (typically 50-500 dimensions).
However, it is difficult to interpret these dimensions in a meaningful manner,
and creating neural embeddings requires extensive training and tuning of
multiple parameters and hyperparameters. We present here a simple unsupervised
method for representing words, phrases or text as a low dimensional vector, in
which the meaning and relative importance of dimensions is transparent to
inspection. We have created a near-comprehensive vector representation of
words, and selected bigrams, trigrams and abbreviations, using the set of
titles and abstracts in PubMed as a corpus. This vector is used to create
several novel implicit word-word and text-text similarity metrics. The implicit
word-word similarity metrics correlate well with human judgement of word pair
similarity and relatedness, and outperform or equal all other reported methods
on a variety of biomedical benchmarks, including several implementations of
neural embeddings trained on PubMed corpora. Our implicit word-word metrics
capture different aspects of word-word relatedness than word2vec-based metrics
and are only partially correlated (rho = ~0.5-0.8 depending on task and
corpus). The vector representations of words, bigrams, trigrams, abbreviations,
and PubMed title+abstracts are all publicly available from
http://arrowsmith.psych.uic.edu for release under CC-BY-NC license. Several
public web query interfaces are also available at the same site, including one
which allows the user to specify a given word and view its most closely related
terms according to direct co-occurrence as well as different implicit
similarity metrics.
Toward Continual Learning for Conversational Agents
Authors: Sungjin Lee
Date: 2017-12-28 00:00:00
Tasks: Continual Learning

While end-to-end neural conversation models have led to promising advances in
reducing hand-crafted features and errors induced by the traditional complex
system architecture, they typically require an enormous amount of data due to
the lack of modularity. Previous studies adopted a hybrid approach with
knowledge-based components either to abstract out domain-specific information
or to augment data to cover more diverse patterns. On the contrary, we propose
to directly address the problem using recent developments in the space of
continual learning for neural models. Specifically, we adopt a
domain-independent neural conversational model and introduce a novel neural
continual learning algorithm that allows a conversational agent to accumulate
skills across different tasks in a data-efficient way. To the best of our
knowledge, this is the first work that applies continual learning to
conversation systems. We verified the efficacy of our method through a
conversational skill transfer from either synthetic dialogs or human-human
dialogs to human-computer conversations in a customer support domain.
Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients
Authors: Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel Chang, Daniel L. Rubin
Date: 2018-01-09 00:00:00

We propose a deep learning model - Probabilistic Prognostic Estimates of
Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term
life expectancy (3 months) of the patients by analyzing free-text clinical
notes in the electronic medical record, while maintaining the temporal visit
sequence. In a single framework, we integrated semantic data mapping and neural
embedding technique to produce a text processing method that extracts relevant
information from heterogeneous types of clinical notes in an unsupervised
manner, and we designed a recurrent neural network to model the temporal
dependency of the patient visits. The model was trained on a large dataset
(10,293 patients) and validated on a separated dataset (1818 patients). Our
method achieved an area under the ROC curve (AUC) of 0.89. To provide
explain-ability, we developed an interactive graphical tool that may improve
physician understanding of the basis for the model's predictions. The high
accuracy and explain-ability of the PPES-Met model may enable our model to be
used as a decision support tool to personalize metastatic cancer treatment and
provide valuable assistance to the physicians.
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
Authors: Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang, Jason H. Moore
Date: 2018-01-09 00:00:00

In this paper a novel biclustering algorithm based on artificial intelligence
(AI) is introduced. The method called EBIC aims to detect biologically
meaningful, order-preserving patterns in complex data. The proposed algorithm
is probably the first one capable of discovering with accuracy exceeding 50%
multiple complex patterns in real gene expression datasets. It is also one of
the very few biclustering methods designed for parallel environments with
multiple graphics processing units (GPUs). We demonstrate that EBIC outperforms
state-of-the-art biclustering methods, in terms of recovery and relevance, on
both synthetic and genetic datasets. EBIC also yields results over 12 times
faster than the most accurate reference algorithms. The proposed algorithm is
anticipated to be added to the repertoire of unsupervised machine learning
algorithms for the analysis of datasets, including those from large-scale
genomic studies.
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention
Authors: Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik
Date: 2018-01-09 00:00:00
Tasks: Stance Detection

The topical stance detection problem addresses detecting the stance of the
text content with respect to a given topic: whether the sentiment of the given
text content is in FAVOR of (positive), is AGAINST (negative), or is NONE
(neutral) towards the given topic. Using the concept of attention, we develop a
two-phase solution. In the first phase, we classify subjectivity - whether a
given tweet is neutral or subjective with respect to the given topic. In the
second phase, we classify sentiment of the subjective tweets (ignoring the
neutral tweets) - whether a given subjective tweet has a FAVOR or AGAINST
stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep
neural network for each phase, and embed attention at each of the phases. On
the SemEval 2016 stance detection Twitter task dataset, we obtain a best-case
macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the
existing deep learning based solutions. Our framework, T-PAN, is the first in
the topical stance detection literature, that uses deep learning within a
two-phase architecture.
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Authors: Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari
Date: 2017-11-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning

We consider the use of Deep Learning methods for modeling complex phenomena
like those occurring in natural physical processes. With the large amount of
data gathered on these phenomena the data intensive paradigm could begin to
challenge more traditional approaches elaborated over the years in fields like
maths or physics. However, despite considerable successes in a variety of
application domains, the machine learning field is not yet ready to handle the
level of complexity required by such problems. Using an example application,
namely Sea Surface Temperature Prediction, we show how general background
knowledge gained from physics could be used as a guideline for designing
efficient Deep Learning models. In order to motivate the approach and to assess
its generality we demonstrate a formal link between the solution of a class of
differential equations underlying a large family of physical phenomena and the
proposed model. Experiments and comparison with series of baselines including a
state of the art numerical approach is then provided.
An efficient K -means clustering algorithm for massive data
Authors: Marco Capó, Aritz Pérez, Jose A. Lozano
Date: 2018-01-09 00:00:00
Tasks: Clustering

The analysis of continously larger datasets is a task of major importance in
a wide variety of scientific fields. In this sense, cluster analysis algorithms
are a key element of exploratory data analysis, due to their easiness in the
implementation and relatively low computational cost. Among these algorithms,
the K -means algorithm stands out as the most popular approach, besides its
high dependency on the initial conditions, as well as to the fact that it might
not scale well on massive datasets. In this article, we propose a recursive and
parallel approximation to the K -means algorithm that scales well on both the
number of instances and dimensionality of the problem, without affecting the
quality of the approximation. In order to achieve this, instead of analyzing
the entire dataset, we work on small weighted sets of points that mostly intend
to extract information from those regions where it is harder to determine the
correct cluster assignment of the original instances. In addition to different
theoretical properties, which deduce the reasoning behind the algorithm,
experimental results indicate that our method outperforms the state-of-the-art
in terms of the trade-off between number of distance computations and the
quality of the solution obtained.
Deep Gaussian Processes with Decoupled Inducing Inputs
Authors: Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
Date: 2018-01-09 00:00:00
Tasks: Gaussian Processes

Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian
Processes (GP) that have proven to work effectively on a multiple supervised
regression tasks. They combine the well calibrated uncertainty estimates of GPs
with the great flexibility of multilayer models. In DGPs, given the inputs, the
outputs of the layers are Gaussian distributions parameterized by their means
and covariances. These layers are realized as Sparse GPs where the training
data is approximated using a small set of pseudo points. In this work, we show
that the computational cost of DGPs can be reduced with no loss in performance
by using a separate, smaller set of pseudo points when calculating the
layerwise variance while using a larger set of pseudo points when calculating
the layerwise mean. This enabled us to train larger models that have lower cost
and better predictive performance.
Denotation Extraction for Interactive Learning in Dialogue Systems
Authors: Miroslav Vodolán, Filip Jurčíček
Date: 2018-01-09 00:00:00
Tasks: Question Answering

This paper presents a novel task using real user data obtained in
human-machine conversation. The task concerns with denotation extraction from
answer hints collected interactively in a dialogue. The task is motivated by
the need for large amounts of training data for question answering dialogue
system development, where the data is often expensive and hard to collect.
Being able to collect denotation interactively and directly from users, one
could improve, for example, natural understanding components on-line and ease
the collection of the training data. This paper also presents introductory
results of evaluation of several denotation extraction models including
attention-based neural network approaches.
Automatic Recognition of Facial Displays of Unfelt Emotions
Authors: Kaustubh Kulkarni, Ciprian Adrian Corneanu, Ikechukwu Ofodile, Sergio Escalera, Xavier Baro, Sylwia Hyniewska, Juri Allik, Gholamreza Anbarjafari
Date: 2017-07-13 00:00:00
Tasks: Emotion Recognition, Video Emotion Recognition

Humans modify their facial expressions in order to communicate their internal
states and sometimes to mislead observers regarding their true emotional
states. Evidence in experimental psychology shows that discriminative facial
responses are short and subtle. This suggests that such behavior would be
easier to distinguish when captured in high resolution at an increased frame
rate. We are proposing SASE-FE, the first dataset of facial expressions that
are either congruent or incongruent with underlying emotion states. We show
that overall the problem of recognizing whether facial movements are
expressions of authentic emotions or not can be successfully addressed by
learning spatio-temporal representations of the data. For this purpose, we
propose a method that aggregates features along fiducial trajectories in a
deeply learnt space. Performance of the proposed model shows that on average it
is easier to distinguish among genuine facial expressions of emotion than among
unfelt facial expressions of emotion and that certain emotion pairs such as
contempt and disgust are more difficult to distinguish than the rest.
Furthermore, the proposed methodology improves state of the art results on CK+
and OULU-CASIA datasets for video emotion recognition, and achieves competitive
results when classifying facial action units on BP4D datase.
Towards social pattern characterization in egocentric photo-streams
Authors: Maedeh Aghaei, Mariella Dimiccoli, Cristian Canton Ferrer, Petia Radeva
Date: 2017-09-05 00:00:00
Tasks: Diversity, Time Series, Time Series Analysis

Following the increasingly popular trend of social interaction analysis in
egocentric vision, this manuscript presents a comprehensive study for automatic
social pattern characterization of a wearable photo-camera user, by relying on
the visual analysis of egocentric photo-streams. The proposed framework
consists of three major steps. The first step is to detect social interactions
of the user where the impact of several social signals on the task is explored.
The detected social events are inspected in the second step for categorization
into different social meetings. These two steps act at event-level where each
potential social event is modeled as a multi-dimensional time-series, whose
dimensions correspond to a set of relevant features for each task, and LSTM is
employed to classify the time-series. The last step of the framework is to
characterize social patterns, which is essentially to infer the diversity and
frequency of the social relations of the user through discovery of recurrences
of the same people across the whole set of social events of the user.
Experimental evaluation over a dataset acquired by 9 users demonstrates
promising results on the task of social pattern characterization from
egocentric photo-streams.
DeepCorrect: Correcting DNN models against Image Distortions
Authors: Tejas Borkar, Lina Karam
Date: 2017-05-05 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object Recognition, Scene Classification

In recent years, the widespread use of deep neural networks (DNNs) has
facilitated great improvements in performance for computer vision tasks like
image classification and object recognition. In most realistic computer vision
applications, an input image undergoes some form of image distortion such as
blur and additive noise during image acquisition or transmission. Deep networks
trained on pristine images perform poorly when tested on such distortions. In
this paper, we evaluate the effect of image distortions like Gaussian blur and
additive noise on the activations of pre-trained convolutional filters. We
propose a metric to identify the most noise susceptible convolutional filters
and rank them in order of the highest gain in classification accuracy upon
correction. In our proposed approach called DeepCorrect, we apply small stacks
of convolutional layers with residual connections, at the output of these
ranked filters and train them to correct the worst distortion affected filter
activations, whilst leaving the rest of the pre-trained filter outputs in the
network unchanged. Performance results show that applying DeepCorrect models
for common vision tasks like image classification (ImageNet), object
recognition (Caltech-101, Caltech-256) and scene classification (SUN-397),
significantly improves the robustness of DNNs against distorted images and
outperforms other alternative approaches..
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"
Authors: Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler
Date: 2018-01-09 00:00:00
Tasks: Density Estimation, Gaussian Processes

We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags, bandwidths for smoothing kernels, as well as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
Authors: Yongshuai Liu, Jiyu Chen, Hao Chen
Date: 2018-01-09 00:00:00

Deep neural networks are vulnerable to adversarial examples. Prior defenses
attempted to make deep networks more robust by either changing the network
architecture or augmenting the training set with adversarial examples, but both
have inherent limitations. Motivated by recent research that shows outliers in
the training set have a high negative influence on the trained model, we
studied the relationship between model robustness and the quality of the
training set. We first show that outliers give the model better generalization
ability but weaker robustness. Next, we propose an adversarial example
detection framework, in which we design two methods for removing outliers from
training set to obtain the sanitized model and then detect adversarial example
by calculating the difference of outputs between the original and the sanitized
model. We evaluated the framework on both MNIST and SVHN. Based on the
difference measured by Kullback-Leibler divergence, we could detect adversarial
examples with accuracy between 94.67% to 99.89%.