pwc-archive/papers-with-abstracts huggingface dataset:
A Constrained Coupled Matrix-Tensor Factorization for Learning Time-evolving and Emerging Topics
Authors: Sanaz Bahargam, Evangelos E. Papalexakis
Date: 2018-06-30 00:00:00

Topic discovery has witnessed a significant growth as a field of data mining
at large. In particular, time-evolving topic discovery, where the evolution of
a topic is taken into account has been instrumental in understanding the
historical context of an emerging topic in a dynamic corpus. Traditionally,
time-evolving topic discovery has focused on this notion of time. However,
especially in settings where content is contributed by a community or a crowd,
an orthogonal notion of time is the one that pertains to the level of expertise
of the content creator: the more experienced the creator, the more advanced the
topic. In this paper, we propose a novel time-evolving topic discovery method
which, in addition to the extracted topics, is able to identify the evolution
of that topic over time, as well as the level of difficulty of that topic, as
it is inferred by the level of expertise of its main contributors. Our method
is based on a novel formulation of Constrained Coupled Matrix-Tensor
Factorization, which adopts constraints well-motivated for, and, as we
demonstrate, are essential for high-quality topic discovery. We qualitatively
evaluate our approach using real data from the Physics and also Programming
Stack Exchange forum, and we were able to identify topics of varying levels of
difficulty which can be linked to external events, such as the announcement of
gravitational waves by the LIGO lab in Physics forum. We provide a quantitative
evaluation of our method by conducting a user study where experts were asked to
judge the coherence and quality of the extracted topics. Finally, our proposed
method has implications for automatic curriculum design using the extracted
topics, where the notion of the level of difficulty is necessary for the proper
modeling of prerequisites and advanced concepts.
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
Authors: Yong Liu, Ruiping Wang, Shiguang Shan, Xilin Chen
Date: 2018-06-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, object-detection, Object Detection

Context is important for accurate visual recognition. In this work we propose
an object detection algorithm that not only considers object visual appearance,
but also makes use of two kinds of context including scene contextual
information and object relationships within a single image. Therefore, object
detection is regarded as both a cognition problem and a reasoning problem when
leveraging these structured information. Specifically, this paper formulates
object detection as a problem of graph structure inference, where given an
image the objects are treated as nodes in a graph and relationships between the
objects are modeled as edges in such graph. To this end, we present a so-called
Structure Inference Network (SIN), a detector that incorporates into a typical
detection framework (e.g. Faster R-CNN) with a graphical model which aims to
infer object state. Comprehensive experiments on PASCAL VOC and MS COCO
datasets indicate that scene context and object relationships truly improve the
performance of object detection with more desirable and reasonable outputs.
Generating Titles for Web Tables
Authors: Braden Hancock, Hongrae Lee, Cong Yu
Date: 2018-06-30 00:00:00
Tasks: Decoder, Descriptive, Text Generation

Descriptive titles provide crucial context for interpreting tables that are extracted from web pages and are a key component of table-based web applications. Prior approaches have attempted to produce titles by selecting existing text snippets associated with the table. These approaches, however, are limited by their dependence on suitable titles existing a priori. In our user study, we observe that the relevant information for the title tends to be scattered across the page, and often--more than 80% of the time--does not appear verbatim anywhere in the page. We propose instead the application of a sequence-to-sequence neural network model as a more generalizable means of generating high-quality titles. This is accomplished by extracting many text snippets that have potentially relevant information to the table, encoding them into an input sequence, and using both copy and generation mechanisms in the decoder to balance relevance and readability of the generated title. We validate this approach with human evaluation on sample web tables and report that while sequence models with only a copy mechanism or only a generation mechanism are easily outperformed by simple selection-based baselines, the model with both capabilities outperforms them all, approaching the quality of crowdsourced titles while training on fewer than ten thousand examples. To the best of our knowledge, the proposed technique is the first to consider text generation methods for table titles and establishes a new state of the art.
Probabilistic Bisection with Spatial Metamodels
Authors: Sergio Rodriguez, Mike Ludkovski
Date: 2018-06-30 00:00:00
Tasks: Active Learning, Gaussian Processes, regression

Probabilistic Bisection Algorithm performs root finding based on knowledge
acquired from noisy oracle responses. We consider the generalized PBA setting
(G-PBA) where the statistical distribution of the oracle is unknown and
location-dependent, so that model inference and Bayesian knowledge updating
must be performed simultaneously. To this end, we propose to leverage the
spatial structure of a typical oracle by constructing a statistical surrogate
for the underlying logistic regression step. We investigate several
non-parametric surrogates, including Binomial Gaussian Processes (B-GP),
Polynomial, Kernel, and Spline Logistic Regression. In parallel, we develop
sampling policies that adaptively balance learning the oracle distribution and
learning the root. One of our proposals mimics active learning with B-GPs and
provides a novel look-ahead predictive variance formula. The resulting gains of
our Spatial PBA algorithm relative to earlier G-PBA models are illustrated with
synthetic examples and a challenging stochastic root finding problem from
Bermudan option pricing.
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry
Authors: Min Zhang, Qianli Ma, Chengfeng Wen, Hai Chen, Deruo Liu, Xianfeng GU, Jie He, Xiaoyin Xu
Date: 2018-06-30 00:00:00
Tasks: Computed Tomography (CT), General Classification, Lung Nodule Classification, Specificity

Lung nodules are commonly detected in screening for patients with a risk for
lung cancer. Though the status of large nodules can be easily diagnosed by fine
needle biopsy or bronchoscopy, small nodules are often difficult to classify on
computed tomography (CT). Recent works have shown that shape analysis of lung
nodules can be used to differentiate benign lesions from malignant ones, though
existing methods are limited in their sensitivity and specificity. In this work
we introduced a new 3D shape analysis within the framework of differential
geometry to calculate the Wasserstein distance between benign and malignant
lung nodules to derive an accurate classification scheme. The Wasserstein
distance between the nodules is calculated based on our new spherical optimal
mass transport, this new algorithm works directly on sphere by using spherical
metric, which is much more accurate and efficient than previous methods. In the
process of deformation, the area-distortion factor gives a probability measure
on the unit sphere, which forms the Wasserstein space. From known cases of
benign and malignant lung nodules, we can calculate a unique optimal mass
transport map between their correspondingly deformed Wasserstein spaces. This
transportation cost defines the Wasserstein distance between them and can be
used to classify new lung nodules into either the benign or malignant class. To
the best of our knowledge, this is the first work that utilizes Wasserstein
distance for lung nodule classification. The advantages of Wasserstein distance
are it is invariant under rigid motions and scalings, thus it intrinsically
measures shape distance even when the underlying shapes are of high complexity,
making it well suited to classify lung nodules as they have different sizes,
orientations, and appearances.
A Learning Theory in Linear Systems under Compositional Models
Authors: Se Un Park
Date: 2018-06-29 00:00:00
Tasks: Learning Theory, Unity

We present a learning theory for the training of a linear system operator
having an input compositional variable and propose a Bayesian inversion method
for inferring the unknown variable from an output of a noisy linear system. We
assume that we have partial or even no knowledge of the operator but have
training data of input and ouput. A compositional variable satisfies the
constraints that the elements of the variable are all non-negative and sum to
unity. We quantified the uncertainty in the trained operator and present the
convergence rates of training in explicit forms for several interesting cases
under stochastic compositional models. The trained linear operator with the
covariance matrix, estimated from the training set of pairs of ground-truth
input and noisy output data, is further used in evaluation of posterior
uncertainty of the solution. This posterior uncertainty clearly demonstrates
uncertainty propagation from noisy training data and addresses possible
mismatch between the true operator and the estimated one in the final solution.
Topology classification with deep learning to improve real-time event selection at the LHC
Authors: Thong Q. Nguyen, Daniel Weitekamp III, Dustin Anderson, Roberto Castello, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
Date: 2018-06-29 00:00:00
Tasks: General Classification

We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments.
Amanuensis: The Programmer's Apprentice
Authors: Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong
Date: 2018-06-29 00:00:00

This document provides an overview of the material covered in a course taught
at Stanford in the spring quarter of 2018. The course draws upon insight from
cognitive and systems neuroscience to implement hybrid connectionist and
symbolic reasoning systems that leverage and extend the state of the art in
machine learning by integrating human and machine intelligence. As a concrete
example we focus on digital assistants that learn from continuous dialog with
an expert software engineer while providing initial value as powerful
analytical, computational and mathematical savants. Over time these savants
learn cognitive strategies (domain-relevant problem solving skills) and develop
intuitions (heuristics and the experience necessary for applying them) by
learning from their expert associates. By doing so these savants elevate their
innate analytical skills allowing them to partner on an equal footing as
versatile collaborators - effectively serving as cognitive extensions and
digital prostheses, thereby amplifying and emulating their human partner's
conceptually-flexible thinking patterns and enabling improved access to and
control over powerful computing resources.
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates
Authors: Joo-Kyung Kim, Young-Bum Kim
Date: 2018-06-29 00:00:00
Tasks: Classification, domain classification, General Classification, Out of Distribution (OOD) Detection

In domain classification for spoken dialog systems, correct detection of
out-of-domain (OOD) utterances is crucial because it reduces confusion and
unnecessary interaction costs between users and the systems. Previous work
usually utilizes OOD detectors that are trained separately from in-domain (IND)
classifiers, and confidence thresholding for OOD detection given target
evaluation scores. In this paper, we introduce a neural joint learning model
for domain classification and OOD detection, where dynamic class weighting is
used during the model training to satisfice a given OOD false acceptance rate
(FAR) while maximizing the domain classification accuracy. Evaluating on two
domain classification tasks for the utterances from a large spoken dialogue
system, we show that our approach significantly improves the domain
classification performance with satisficing given target FARs.
Fully Nonparametric Bayesian Additive Regression Trees
Authors: Edward George, Prakash Laud, Brent Logan, Robert McCulloch, Rodney Sparapani
Date: 2018-06-29 00:00:00
Tasks: regression, Uncertainty Quantification

Bayesian Additive Regression Trees (BART) is a fully Bayesian approach to
modeling with ensembles of trees. BART can uncover complex regression functions
with high dimensional regressors in a fairly automatic way and provide Bayesian
quantification of the uncertainty through the posterior. However, BART assumes
IID normal errors. This strong parametric assumption can lead to misleading
inference and uncertainty quantification. In this paper, we use the classic
Dirichlet process mixture (DPM) mechanism to nonparametrically model the error
distribution. A key strength of BART is that default prior settings work
reasonably well in a variety of problems. The challenge in extending BART is to
choose the parameters of the DPM so that the strengths of the standard BART
approach is not lost when the errors are close to normal, but the DPM has the
ability to adapt to non-normal errors.
Adversarial Examples in Deep Learning: Characterization and Divergence
Authors: Wenqi Wei, Ling Liu, Margaret Loper, Stacey Truex, Lei Yu, Mehmet Emre Gursoy, Yanzhao Wu
Date: 2018-06-29 00:00:00
Tasks: Adversarial Attack, Deep Learning

The burgeoning success of deep learning has raised the security and privacy
concerns as more and more tasks are accompanied with sensitive data.
Adversarial attacks in deep learning have emerged as one of the dominating
security threat to a range of mission-critical deep learning systems and
applications. This paper takes a holistic and principled approach to perform
statistical characterization of adversarial examples in deep learning. We
provide a general formulation of adversarial examples and elaborate on the
basic principle for adversarial attack algorithm design. We introduce easy and
hard categorization of adversarial attacks to analyze the effectiveness of
adversarial examples in terms of attack success rate, degree of change in
adversarial perturbation, average entropy of prediction qualities, and fraction
of adversarial examples that lead to successful attacks. We conduct extensive
experimental study on adversarial behavior in easy and hard attacks under deep
learning models with different hyperparameters and different deep learning
frameworks. We show that the same adversarial attack behaves differently under
different hyperparameters and across different frameworks due to the different
features learned under different deep learning model training process. Our
statistical characterization with strong empirical evidence provides a
transformative enlightenment on mitigation strategies towards effective
countermeasures against present and future adversarial attacks.
AI in Game Playing: Sokoban Solver
Authors: Anand Venkatesan, Atishay Jain, Rakesh Grewal
Date: 2018-06-29 00:00:00
Tasks: AI Agent, Sokoban

Artificial Intelligence is becoming instrumental in a variety of
applications. Games serve as a good breeding ground for trying and testing
these algorithms in a sandbox with simpler constraints in comparison to real
life. In this project, we aim to develop an AI agent that can solve the
classical Japanese game of Sokoban using various algorithms and heuristics and
compare their performances through standard metrics.
It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems
Authors: Jelena Milosevic, Dexmont Pena, Andrew Forembsky, David Moloney, Miroslaw Malek
Date: 2018-06-29 00:00:00
Tasks: All, Emotion Recognition

While several approaches to face emotion recognition task are proposed in
literature, none of them reports on power consumption nor inference time
required to run the system in an embedded environment. Without adequate
knowledge about these factors it is not clear whether we are actually able to
provide accurate face emotion recognition in the embedded environment or not,
and if not, how far we are from making it feasible and what are the biggest
bottlenecks we face.
  The main goal of this paper is to answer these questions and to convey the
message that instead of reporting only detection accuracy also power
consumption and inference time should be reported as real usability of the
proposed systems and their adoption in human computer interaction strongly
depends on it. In this paper, we identify the state-of-the art face emotion
recognition methods that are potentially suitable for embedded environment and
the most frequently used datasets for this task. Our study shows that most of
the performed experiments use datasets with posed expressions or in a
particular experimental setup with special conditions for image collection.
Since our goal is to evaluate the performance of the identified promising
methods in the realistic scenario, we collect a new dataset with
non-exaggerated emotions and we use it, in addition to the publicly available
datasets, for the evaluation of detection accuracy, power consumption and
inference time on three frequently used embedded devices with different
computational capabilities. Our results show that gray images are still more
suitable for embedded environment than color ones and that for most of the
analyzed systems either inference time or energy consumption or both are
limiting factor for their adoption in real-life embedded applications.
Neural Networks Trained to Solve Differential Equations Learn General Representations
Authors: Martin Magill, Faisal Qureshi, Hendrick W. de Haan
Date: 2018-06-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Transfer Learning

We introduce a technique based on the singular vector canonical correlation
analysis (SVCCA) for measuring the generality of neural network layers across a
continuously-parametrized set of tasks. We illustrate this method by studying
generality in neural networks trained to solve parametrized boundary value
problems based on the Poisson partial differential equation. We find that the
first hidden layer is general, and that deeper layers are successively more
specific. Next, we validate our method against an existing technique that
measures layer generality using transfer learning experiments. We find
excellent agreement between the two methods, and note that our method is much
faster, particularly for continuously-parametrized problems. Finally, we
visualize the general representations of the first layers, and interpret them
as generalized coordinates over the input domain.
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Authors: Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You
Date: 2018-06-29 00:00:00
Tasks: Fairness

Classifiers can be trained with data-dependent constraints to satisfy
fairness goals, reduce churn, achieve a targeted false positive rate, or other
policy goals. We study the generalization performance for such constrained
optimization problems, in terms of how well the constraints are satisfied at
evaluation time, given that they are satisfied at training time. To improve
generalization performance, we frame the problem as a two-player game where one
player optimizes the model parameters on a training dataset, and the other
player enforces the constraints on an independent validation dataset. We build
on recent work in two-player constrained optimization to show that if one uses
this two-dataset approach, then constraint generalization can be significantly
improved. As we illustrate experimentally, this approach works not only in
theory, but also in practice.
High Dimensional Discrete Integration over the Hypergrid
Authors: Raj Kumar Maity, Arya Mazumdar, Soumyabrata Pal
Date: 2018-06-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Recently Ermon et al. (2013) pioneered a way to practically compute approximations to large scale counting or discrete integration problems by using random hashes. The hashes are used to reduce the counting problem into many separate discrete optimization problems. The optimization problems then can be solved by an NP-oracle such as commercial SAT solvers or integer linear programming (ILP) solvers. In particular, Ermon et al. showed that if the domain of integration is $\{0,1\}^n$ then it is possible to obtain a solution within a factor of $16$ of the optimal (a 16-approximation) by this technique. In many crucial counting tasks, such as computation of partition function of ferromagnetic Potts model, the domain of integration is naturally $\{0,1,\dots, q-1\}^n, q>2$, the hypergrid. The straightforward extension of Ermon et al.'s method allows a $q^2$-approximation for this problem. For large values of $q$, this is undesirable. In this paper, we show an improved technique to obtain an approximation factor of $4+O(1/q^2)$ to this problem. We are able to achieve this by using an idea of optimization over multiple bins of the hash functions, that can be easily implemented by inequality constraints, or even in unconstrained way. Also the burden on the NP-oracle is not increased by our method (an ILP solver can still be used). We provide experimental simulation results to support the theoretical guarantees of our algorithms.